tag:blogger.com,1999:blog-208978502024-03-05T00:19:31.389-05:00Stock Trends ReporterStock Trends - Wealth-building stock market analysis and stock trading strategies
A unique stock market trend analysis tool for investors with free stock quotes, free stock trends report, stock charts and stock trends indicators for over 7,000 stocks.Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.comBlogger299125tag:blogger.com,1999:blog-20897850.post-86177587604573784142018-09-17T21:58:00.001-04:002021-05-03T10:15:26.130-04:00Random Portfolio benchmarks<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
The growth of passive investing has a lot to do with investors migrating to lower-cost investment vehicles, but implicit in the migration to passive frameworks is a presumably lower risk metric. Investors want to be exposed to equities but don’t want to take risks beyond those inherent in the benchmark indexes they are investing in. Additionally, index investors are opting out of the risks of active management because the returns generated have recently not kept pace with the index benchmarks. But indexes are not what investors should be using as benchmarks. They don’t represent a truly passive approach to the market. The only truly passive approach to the markets is one that employs random portfolio construction.<br />
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Why random portfolios? Because all indexes are systematically biased by their factor premises. The prevalent index factor is size (market capitalization) but other factor frameworks include sector, industry, value, growth, momentum, and smart beta. Each of these index frameworks employs a systematic weighting of components based on a predetermined valuation that aims to minimize variability of returns based on defined factors.<br />
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Take a look at the S&P 500 index. It’s the most traded index in the world - through ETFs like SPY - but more importantly is the primary benchmark for U.S. equities. The performance S&P 500 index guides investors in terms of relative performance of actively managed funds and ultimately is the most broadly used compensation metric of the asset management industry. The fees investors pay these managers and, ultimately, the employment, compensation and rewards these fees fuel depend on the structure of the index.<br />
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The S&P 500 index is first and foremost a members club. Stocks are included in the index by decision of a committee. Yes, constituent stocks must meet certain primary criteria - market capitalization (float-adjusted weightings), liquidity, domicile, public float, sector classification, financial viability, and length of time publicly traded and stock exchange - but the criteria is set by a member board. And it’s set with a certain purpose in mind: the index is a gauge of large cap U.S. equities.<br />
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So we know that the SPX is a factor-based measure of U.S. equities - it tells us about the price movement of large-cap stocks. That should surprise few people. Implicit in this size factor is the fact that the SPX accounts for about 80% of the entire capitalization of the U.S. stock market. That’s a good chunk of the assets invested in this market.<br />
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However, the size factor weighting of the SPX is problematic when it comes to fulfilling the index’s role as a benchmark. The size bias distorts the benchmark performance and concentrates on the largest companies, often adding additional risk in those constituents because many are in the same sector or industry. In fact, the current weighting of the SPX shows that about 28% of the index weighting is in technology stocks. Further, the top 4 stocks by weight in the index are Apple, Microsoft, Amazon, and Facebook. The S&P 500 index has a decidedly tech bias at this time, a considerable sector risk for a benchmark index that is ostensibly supposed to track the overall performance of U.S. equities.<br />
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That may be a reasonably correct weighting of big cap U.S. equities, however, it’s not a correct benchmark measure of alpha. Alpha is the intelligence that extracts investment returns above the market performance. A portfolio of stocks constructed with a size bias only tells us about the performance of the bias. It does not represent the performance of a naive portfolio - a portfolio of stocks that has no factor bias. A naive portfolio would not have a selection criteria that restricts to a subset of a given universe of stocks. It would be a portfolio derived from a set of randomly selected stocks.<br />
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Random portfolio returns give us an estimate of the returns that are built into the broad market. Irrespective of factors that deliver alpha, a measure of random portfolio returns tells us the returns a given market generates without having any specific intelligence about how to generate those returns. They are the returns that would be generated by an untrained monkey.<br />
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What have been the random portfolio returns of the U.S. equity market? How do they compare to the returns of the S&P 500 index? Below is an annual comparison of 52-week returns (%) of the market cap index and the mean (average) return (%) of randomly selected portfolios. The last column shows the differences in returns (%) between the market cap index (SPX) and the random portfolios.<br />
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The random portfolio returns are the mean return (%) of 1,000 randomly selected portfolios of 100 common stocks selected at the beginning of the return period. That would be equivalent to 1,000 different monkeys picking a 100 stock portfolio, then taking the average return of those 1,000 portfolios after the 52-week period. The universe of stocks from which these random portfolios are selected includes all NYSE/Nasdaq listed common stocks that have traded for at least 40-weeks trading at a price above $2 and weekly trading volume above 100,000 shares. That would be a universe of about 3,900 stocks.</div>
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<strong>Annual returns (%) - SPX and random portfolios</strong></div>
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This summary tells us that the S&P 500 index has only out-performed the random portfolios in 7 of the last 15 years (2003-2017) and that the sum of the differences in return is -26.3%. The monkey would have outperformed the S&P 500 index over the period by a significant amount. However, we can also see that much of this outperformance comes in 2003 and 2009, both years where the overall market enjoyed exceptional returns after a bear market. Clearly, those were periods where the size bias of the S&P 500 index excluded it from returns that were delivered elsewhere in the market (small-cap stocks, growth stocks, momentum stocks, etc.).<br />
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Presently, the S&P 500 index is providing excess returns above the benchmark represented by random portfolios. On the surface that tells us that portfolios weighted toward large-cap - specifically large-cap technology stocks - are outperforming the market. They will until they don’t. Investors should be aware of where their returns are coming from and where their risk is situated. The S&P 500 index is not a passive index. It is not market agnostic.</div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-21324817072562597832017-04-25T21:01:00.001-04:002021-05-03T10:11:20.210-04:00Stock Trends Slots game!<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="background-color: transparent; color: black; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">There is a new Stock Trends learning application installed on the Stock Trends website - a slots game! The <a href="https://stocktrends.com/learn/stock-trends-slots-game" target="_blank">Stock Trends Slots</a> game engages users by generating random sets of stocks and matching combinations of their Stock Trends indicators. These matching combinations score points based on the probabilities of the match. Every week a new set of Stock Trends indicators reflect the changing stock market, so the game’s probabilities and rewards change with the distribution of the indicators. </span></div>
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<span style="background-color: transparent; color: black; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Investors might wonder how a random outcome game applies to the markets. But there are aspects of skill in the game that come with the premium functions subscribers to <a href="https://stocktrends.com/learn/stock-trends-weekly-reporter" target="_blank">Stock Trends Weekly Reporter</a> may access. The ability to lock game rows allows users to improve the probabilities of making a match without a corresponding reduction in reward. That means a skillful player who understands the distribution of trend and price momentum indicators in a current market can achieve higher scores.</span></div>
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<span style="background-color: transparent; color: black; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Each spin of the game creates a random portfolio of five (5) stocks and/or ETFs. This is a useful application of random portfolio generation that also makes for an interesting stock market game. These random portfolios - generated by the Stock Trends Slots game play - can be entered into the <a href="https://stocktrends.com/learn/stock-trends-investor-challenge" target="_blank">Stock Trends Investor Challenge</a>, a weekly stock market competition to see which portfolio has the best return after 4-weeks of actual market activity. </span></div>
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<span style="background-color: transparent; color: black; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">This stock market game helps illustrate how random portfolios perform relative to the market benchmarks. Users have some degree of skills-based intervention in the Stock Trends Investor Challenge as they can either choose to enter their slots game portfolio, or not. The Stock Trends indicators help give guidance on that decision, just as they do for actual stock market analysis.</span></div>
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<br />Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-15483469199826863802017-04-25T18:51:00.001-04:002017-04-25T18:51:35.716-04:00Trend profile - SPDR S&P Metals & Mining ETF $XME<div dir="ltr" style="background-color: white; font-family: Arial, Helvetica, sans-serif; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">Stock prices tend to move in trending patterns. This is a simple idea that may, or may not be supported by evidence. It really depends on how you frame the question and what time frame is presented. That’s because prices also tend to revert to a mean or average price. This interplay of price trend and price reversion is a fundamental dynamic of the market. It’s also the window dressing for evidence of randomness that underpins market price patterns.</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">It’s easy to not recognize randomness when we focus so much on trend and reversion. A core mechanism for human understanding is our ability to identify patterns and formulate responses to them. Indeed, much of our learning is dependent upon pattern recognition. Why shouldn’t our understanding of the markets be based on the same formulations?</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">Many successful trading strategies are based on pattern recognition. Whether fundamental or technical in nature, these systems win when the precepts of their approach match what the market is delivering at any particular span of time. Market is trending: systems based on price trend patterns win. Market is reverting: systems based on price reversion patterns win. A truly intelligent trading system would know how to recognize the difference between the two and when to apply either a price trend system or a price reversion system. (Orpheus Risk Management Indices (RMI) is one such intelligent system </span><a href="http://www.orpheusindices.com/" style="text-decoration: none;"><span style="color: #1155cc; font-family: "arial"; font-size: 11pt; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap;">http://www.orpheusindices.com/</span></a><span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">)</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">However, attempts to predict outcomes in a world of true randomness cannot be absolutely defended, by definition, no matter how intelligent. Looking for patterns in randomness brings us to Chaos theory and fractal mathematics, which explores the transitions between order and disorder in deterministic systems dependent of initial conditions. </span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">This is heady and fascinating stuff that has a growing influence on financial markets analysis, but how does a stockpicker fit in all this? It’s no wonder that the era of the stockpicker is quickly transforming into algorithmic and machine learning systems increasingly favoured by capital markets money managers. That’s all well and good for highly capitalized institutional shops, but what about the little guy?</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">The <strong>Stock Trends Inference Model</strong> (STIM) is an attempt to reconcile randomness in the market with evidence of price patterns. It is a simple application of statistics to Stock Trends categorical indicators that answers some basic questions about certain trend characteristics. Examples of these questions include: If the price momentum of a stock is relatively high and it has been in a bullish trend for a relatively long period, will the price momentum continue, and for how long? If a price trend has changed from bearish to bullish, what are expectations for price momentum going forward? If a stock breaks out of a bearish trend, what are the probabilities it will it retreat?</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">All of these questions are asked with the assumption that the answers provided are independent of the present broad market condition. That is, we want to know return expectations regardless of whether the market is in a bull or bear trend. Why? Because we cannot know whether the present trend of the market will persist. If we make an assumption that it will, then our measurement of the expected returns of an individual market (stock, ETF) will be imprecise. </span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">This is important. When we take a measurement of a particular market condition - as represented in the combination of Stock Trends indicators in each weekly Stock Trends Report for individual stocks and ETFs - the observations of similar market conditions will take place across time periods that span the entire population of observations. Each observation occurs in varying broad market trend phases. In this respect, the Stock Trends Inference Model is broad market agnostic. </span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">For that reason, the base measurement of returns is relative to the historical random returns of stocks, which is about 8% annually, and specifically equate to the following expected returns for each of the relevant periods Stock Trends measures: 0% 4-week return, 2.19% 13-week return, and a 6.45% 40-week return. If a trend condition for a particular stock/ETF does not provide statistical evidence that it can beat these base return expectations, then we cannot say anything definitive about its return expectations. However, if there is a deviation from the base return expectations we can say that the current trend characteristics indicate either over-performance or under-performance projections. This is the objective of the Stock Trends Inference Model.</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">A good starting point for Stock Trends Weekly Reporter subscribers is a weekly review of the <strong>STIM Select stocks</strong> report. It shows the stocks and ETFs that have the best statistical trend characteristics. The report is ranked by the 13-week return expectations. </span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;"><img alt="" height="1256" src="https://www.stocktrends.com/userfiles/image/ArticlesImages/2017/20170424/20170424_editorial_STIM_NYSE.png" width="856" /></span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">The current NYSE STIM Select report, as an example, includes the SPDR S&P Metals & Mining ETF (<a href="http://www.stocktrends.com/streport.php?symbol=XME-N&ref=sm">XME-N</a>). The Stock Trends Report for XME shows that the ETF is 7-weeks into a Weak Bullish trend; that it is underperforming the S&P 500 index by 12% over the past 13-weeks and underperformed the broad market index last week (RSI 88 - ). It’s been in a Bullish category for 54-weeks but has been retreating since February.</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;"><img alt="SPDR S&P Metals & Mining ETF $XME - Stock Trends Report" height="444" src="https://www.stocktrends.com/userfiles/image/ArticlesImages/2017/20170424/20170424_editorial_ST_XME.png" width="751" /></span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">The statistical model shows that there have been about 277 observations of stocks and ETFs that have shared these characteristics or have had similar Stock Trends indicator combinations. From this sample we can make inferences about the expected returns of XME over the next 4-week, 13-week, and 40-week periods.</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;"><img alt="SPDR S&P Metals & Mining ETF $XME - estimated returns STIM" height="1041" src="https://www.stocktrends.com/userfiles/image/ArticlesImages/2017/20170424/20170424_editorial_STIM_XME.png" width="721" /></span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">The green sample density plots show the distribution of returns for the three separate periods following the observation. Most generally, these distributions will be centered around the mean random return expected for each period ( 0% 4-week return, 2.19% 13-week return, 6.45% 40-week return). However, certain Stock Trends indicator combinations yield sample distributions that deviate from the expected mean random returns. The sample distribution of returns generated in the XME sample deviate in a positive way.</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">For the 4-week period 53.8% of returns in the sample are greater than 0%, the expected 4-week return. By employing statistical inference methods to estimate the population mean, we can estimate that the expected (or mean) 4-week return for XME is 1.8%. More importantly, with our assumption of a normal distribution of returns - a defining attribute of randomness - we also can estimate that XME has a 56.5% probability of having a return greater than the expected 4-week return of a randomly selected stock. This in comparison to the 50% probability we would expect from a random stock.</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">Similarly, the 13-week expected return for XME is 7.2%, with a 60.8% probability of besting the base period expected return of 2.19%, and the 40-week expected return for XME is 21%, with a 64% probability of beating the base period expected return of 6.45%. All better probabilities for beating the returns of a randomly selected stock.</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">While even a 64% probability is better than a 50% probability implicit in a random selection, it’s still only a 64% probability. There is a 36% probability that it will underperform the expected return of a randomly selected stock. If you know anything about chance, you must know that a 36% chance of being wrong is more than enough to lose your shirt.</span></div>
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<span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;">However, the Stock Trends Inference Model does tell us that XME is currently in a trend and momentum position that historically has exhibited tendency toward positive returns in the subsequent period. This gives us some confidence in making a directional trade, and can be used as the foundation of a derivatives trade (options) that further improves a trader’s probability of making a profitable trade.</span><span style="font-family: "arial"; font-size: 11pt; vertical-align: baseline; white-space: pre-wrap;"> </span></div>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-48235467378865898592017-02-04T10:21:00.000-05:002017-02-04T10:21:22.495-05:00Stock Trends Slots Game<h1 align="center" style="background-color: white; box-sizing: border-box; color: #333333; font-family: Arial, helvetica; font-size: 36px; font-weight: 500; line-height: 1.1; margin: 20px 0px 10px;">
<a href="https://www.stocktrends.com/slots/slot_stocktrendsgame.php" target="_blank">Stock Trends Slots</a></h1>
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The stock market is a game of chance. Try your luck!</div>
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Stock Trends reports on weekly price and volume changes for thousands of individual North American stocks. Every eligible stock is assigned a series of Stock Trends indicators which interpret those changes. Match these indicators in the <span class="strptheader2" style="box-sizing: border-box; font-weight: 700;">Stock Trends Slots</span> game and learn how the changing market affects random outcomes presented by the game.</div>
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This game of chance is a great way to learn about the Stock Trends indicators and how they provide guidance toward probable outcomes.</div>
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Every week a new data set is created with the Stock Trends updated indicators. The Stock Trends Slots game data for the current week are the records that have Stock Trends indicator values for listed common stock, exchange traded funds, or income trust units on the following four exchanges - New York, Nasdaq, Amex, and Toronto.</div>
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<span style="box-sizing: border-box; font-weight: 700;">How to Play</span></div>
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Player receives a set of 5 random draws to start the game.</div>
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On each draw or play, a random generator fills 5 rows with listings for 5 different stocks, with each record filling the columns in the order of stock symbol, <a href="https://www.stocktrends.com/main.php?page=stTrendsymref.php" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;" target="_blank">Stock Trends trend indicator</a>, <a href="https://www.stocktrends.com/main.php?page=stguideVolume.php" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;" target="_blank">Stock Trends volume indicator</a>, <a href="https://www.stocktrends.com/main.php?page=stguidersi.php" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;" target="_blank">Stock Trends RSI value, and Stock Trends RSI up/down indicator</a>. Learn more about what these indicators mean in the<a href="https://www.stocktrends.com/main.php?page=stLearn.php" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;" target="_blank">Learn</a> section of the Stock Trends website.</div>
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Each draw the player accumulates points and/or free plays with matching columns and rows, as described in the section below. Each week, with the changing market and the new set of Stock Trends indicators published, the probabilities and associated payouts change. See how the market trends affect the game play and how your luck ranks against other players!</div>
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At the end of each slots play players may choose to enter a Bonus feature play - the <a href="https://www.stocktrends.com/slots/stocktrendsinvestorchallenge.php?weeks=4" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;">Stock Trends Investor Challenge</a>! Enter your final portfolio of 5 stocks in the rolling 4-week Stock Trends Investor Challenge for a chance to win BONUS awards if your portfolio beats all challengers! Stock Market Investor Challenge winners are awarded every week. (Note: presently we are beta testing the game - no prizes are applied to winners)</div>
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Subscribers to Stock Trends Weekly Reporter have the option to lock rows before a spin and improve the odds of making another point scoring match. For instance, if a player has matched two rows, he can select those two rows to be locked in position such that in the following spin only the other three rows are randomly selected. The points payout for matches of additional rows remains the same, but the probability of making those matches improves.</div>
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The following rules apply to the locking of rows:</div>
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1) once a row match is locked, no points for the locked match are scored on the subsequent spins. That is, there is no double counting of matches when the matches are locked.</div>
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2) if the player locks more than two rows, the free spin combinations do not receive free spins. There is a cost to locking more than two rows - the player cannot accumulate additional spins with the free spin matches.</div>
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Play to improve your odds and enter your portfolio of stocks <br style="box-sizing: border-box;" />in the <a href="https://www.stocktrends.com/slots/stocktrendsinvestorchallenge.php?weeks=4" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;">Stock Trends Investor Challenge</a>!</div>
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As a subscriber to <span style="box-sizing: border-box; font-weight: 700;">Stock Trends Weekly Reporter</span> you will have access to additional game features:</div>
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<li style="box-sizing: border-box;">lock game rows to reduce random outcomes and improve your odds of making a match!</li>
<li style="box-sizing: border-box;">enter your own custom 5-stock portfolio in the <a href="https://www.stocktrends.com/slots/stocktrendsinvestorchallenge.php?weeks=4" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;">Stock Trends Investor Challenge</a>.</li>
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<a href="https://www.stocktrends.com/slots/slot_stocktrendsgame.php" target="_blank">GO TO STOCKTRENDS.COM TO LEARN MORE AND PLAY!!</a></div>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-26066437421320922702015-06-27T11:30:00.000-04:002015-06-27T11:30:49.045-04:00Are you a systematic investor?<div dir="ltr" style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
To be a successful trader one should be part data scientist. Although there are some highly successful traders that hinge their plan on subjective analysis artforms, the road to long-term profits in active trading should be grounded in the laboratory of data. The simple reason for this: failure, as much as success, must be measured and understood because market outcomes are often inherently volatile and unpredictable. Scientific method allows us to test trading hypothesis, learn from mistakes, and quantify risk. Systematic traders understand that without the integrity of data science, they are simply ticker tape cowboys.</div>
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Having an analysis framework is an important departure point for the systematic investor. That framework could be fundamental / value analysis - translating measures of intrinsic value into trading signals. An example of this would be the Dogs of the Dow trading strategy. For market technicians core intrinsic value relationships are too complex to model completely. Instead, a technical analyst focuses on patterns of supply and demand for an investment instrument. Those patterns of subjective market valuations are revealed in the price and volume of every stock.</div>
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<strong>“It’s not good enough to be anecdotal or doctrinaire when it comes to trading.”</strong></div>
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Market technicians believe in the market’s message. They construct price and volume charts to read the tea leaves, so to speak, about the future direction of a market price. But here’s where this backward-looking artform often fails: how can a technical analyst place any faith in the reading of these charts? It’s not good enough to be anecdotal or doctrinaire when it comes to trading. It’s not good enough to show a tidy chart that reveals, for example, a head and shoulders pattern, and assert a projection for future price movement if there is no data from which to develop a measure of confidence in the prognostication.</div>
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There should be some standard of evidence to support a particular chart reading. If there is no evidence, there’s no foundation for acceptance. A technical trader should quantify the probabilities of future outcomes. Data science allows the diligent systematic investor to develop a level of confidence in a market environment that is fundamentally uncertain. Risk must be quantified!</div>
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How does Stock Trends help us turn stock market data into actionable quantitative measures of confidence? First, the Stock Trends indicators are by definition categorical - they translate market price and volume data into factor variables, or independent variables. In a data science setting we can use these independent variables as inputs and measure a relevant outcome, or output. The significant outcome for investors, of course, is the future return. If a dependent relationship is established between the inputs and the output, the trader can measure a confidence level for a desired trading outcome. Let’s now look at each of the Stock Trends indicators and how they fit our Stock Trends Inference Model.</div>
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The Stock Trends trend categories are the result of a method of translating market price data (quantitative variables) into categorical variables. For instance, last week’s closing price of Apple (<a href="http://www.stocktrends.com/streport.php?symbol=AAPL-Q">AAPL</a>) was $126.60. The Stock Trends trend indicator categorizes that price by applying a framework for qualification and putting the current price into a long-term price context. Using 13-week and 40-week average prices as guideposts, the trend indicator - now Stock Trends Bullish (<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />) - gives us a factor variable for the $126.60 market price.</div>
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A base test, then, would be to measure how a market price performs when it is in this trend category. However, we would want a more granular categorization because within each trend category there are many ancillary variable qualifications. For instance, a Bullish trend category can be relatively new, or it can be quite entrenched.</div>
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That is why Stock Trends publishes trend counters. They give us a better understanding of the time frame of the trend category. In our Apple example we can see that the current Bullish trend category has been in place for 92-weeks, about twice the average length of a typical Bullish trend, and that the current strong Bullish indicator has been in place for 22-weeks. So now we can ask the following question: how have stocks performed when they have been in a Bullish trend category for about 92-weeks, and also in a strong Bullish indicator for the most recent 22-weeks?</div>
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But our granularity can be improved even more. We also recognize that within any trend there are varying levels of price momentum. Stocks rally and retreat. The Stock Trends Relative Strength Indicators provide us with a method for translating price performance into factor inputs. The 13-week RSI values are discrete variables that can be cut into bins of specific ranges of values. By qualifying each stock’s trend by its relative price momentum to the broad market we can now be more specific about the characteristics we are sampling. In the case of our Apple example, its 13-week RSI is 100. This indicates the stock is only performing at par with the S&P 500 over the past 13-weeks. Now we can sample for Bullish stocks that also share this condition.</div>
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The RSI +/- indicator is a binary signal of whether a stock has outperformed or underperformed the broad market in the past week. Again, this indicator can be used as another factor input. Apple underperformed the S&P 500 index last week, and therefore has a (-) indicator.</div>
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Finally, another factor variable that Stock Trends creates is derived from the weekly volume of shares traded. Three different factor levels characterize the weekly volume, so that we can differentiate stocks further by which level the trading volume fits. Last week Apple had neither high nor low volume of trading, so its volume can be characterised as normal.</div>
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With these composite factor variables, published in each Stock Trends Report, the Stock Trends Profile presents the results of the Stock Trends Inference Model. In the case of Apple, shown below, we can see that the current Stock Trends indicators are relatively positive: the future 4-week return of Apple has a 57% probability being higher than the expected mean random return of a stock, which is 0%. Remember, that a randomly chosen stock has a 50% chance of having a 4-week return greater than 0% (see <a href="http://www.stocktrends.com/show_article.php?id=78">The random outcome benchmark</a>). AAPL has a 62.2% chance of besting the base mean 13-week random return, which is 2.19%, and a 56% probability of besting the mean 40-week random return (6.45%) .</div>
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These probabilities might not strike you as significantly positive. However, they do indicate that the trend and momentum conditions for AAPL are sufficiently supportive of a continued bullish stance for Apple investors. The analysis also tells us that AAPL is more appealing than stocks with lower return expectations. You can compare the returns expectations of industry stocks in the associated heatmap that ranks the expected future returns.</div>
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This is the analysis framework of Stock Trends: translating the weekly trading statistics of an issue into factor input variables. It is how we interpret these variables and their significance in predicting future price performance that makes Stock Trends a unique and effective data science application. The Stock Trends Inference Model statistically measures the change in stock price that follows from each market condition defined by the composite of the inputs of each Stock Trends indicator combination.</div>
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Stock Trends covers the North American stock market - thousands of issues every week are categorized by the Stock Trends indicators. Each of these observations since 1980 - now numbering over 9.2-million records - can be used as input variables in models that measure the subsequent price change in the categorized stock. We can ask the question: what kind of returns did a stock have after it was categorized by the Stock Trends indicators? Do stocks that have a Bullish trend indicator and high price momentum perform better, on average, than other stocks? Is there any statistical evidence that momentum trading is profitable? Does a Bullish Crossover offer a good trade entry signal? More broadly, does the data support many of the doctrinaire positions of technical analysis? The Stock Trends Inference Model attempts to answer these type of questions.</div>
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<strong>"Every technical analyst who presents a price chart as evidence of a buy signal must also present a distribution graph of the expected returns. If they don't, take their advice with a grain of salt."</strong></div>
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Stock Trends analysis framework is simple, but specific. It looks at certain important aspects of technical analysis - trend and price momentum. Another analysis framework might be centered on other algorithms of price and volume, and on a different time frame. Every investor has to choose what analysis framework fits their own assumptions about the dependent relationships in the market. However, each analysis framework must be measureable. The litmus test of this measurement should be the the presentation of data, the display of returns distributions. Indeed, in my opinion every technical analyst who presents a price chart as evidence of a buy signal must also present a distribution graph of the expected returns. If they don’t, take their advice with a grain of salt. Your success as a systematic investor will reflect your diligence in making data science integral to your trading strategies.</div>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-8120901598328216672015-05-04T21:34:00.000-04:002015-05-04T21:38:31.870-04:00Return expectations for Twitter $TWTR #notgood<div dir="ltr" style="background-color: white; font-family: Arial, Helvetica, sans-serif; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="font-family: Arial; font-size: 15px; vertical-align: baseline; white-space: pre-wrap;">There’s a new social media button: UnLike. Twitter’s stock (<a href="http://www.stocktrends.com/streport.php?symbol=TWTR-N">TWTR</a>) might be the first click. It’s tumble last week erased much of the first quarter goodwill the market had buffed up, closing at $37.84 on Friday and leaving behind the previous $50 support level in splinters. With any market response of this profile there are hails of panic, as well as resolve. Does an investor see this correction as the beginning of an even nastier fall or an opportunity to take advantage of nervous Nellies?</span></div>
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<span style="font-family: Arial; font-size: 15px; vertical-align: baseline; white-space: pre-wrap;">Technical analysis is, by definition, the study of price and trading activity. It seeks to answer the basic question posed in any market - Do I buy, or do I sell? - by interpreting past market action and prognosticating about future market action. Sometimes the market characteristics of a particular stock (or any trading instrument) are not that distinguishable or distinguishing. And then there are stocks with market activity that is much more categorically defined. Hello, Twitter!</span></div>
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<span style="font-family: Arial; font-size: 15px; vertical-align: baseline; white-space: pre-wrap;">Stock Trends allows us to isolate market characteristics - and especially so when there is a selloff. The 25.5% drop of TWTR last week flushed out many investors, and the usually high volume of trading indicator tells us the scope of this sentiment change is substantial. When we see a change to a Stock Trends Weak Bullish indicator (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" height="14" width="16" />) on this kind of price and volume move the technical aspects of the stock are quite distinguishable. </span><br />
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<span style="font-family: Arial; font-size: 15px; vertical-align: baseline; white-space: pre-wrap;">TWTR’s Stock Trends Report shows a combination of indicators that make this event categorically interesting: the trend indicator is Weak Bullish (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" height="14" width="16" />), with a minor <a href="http://www.stocktrends.com/main.php?page=stTrendCnt.php">trend counter</a> of 1, and a major trend counter now at 6, an <a href="http://www.stocktrends.com/main.php?page=stguidersi.php">RSI</a> of 95 - , and an unusually </span><span style="font-family: Arial; font-size: 15px; line-height: 20.7000007629395px; white-space: pre-wrap;"><a href="http://www.stocktrends.com/main.php?page=stguideVolume.php">high volume indicator</a></span><span style="font-family: Arial; font-size: 15px; line-height: 1.38; white-space: pre-wrap;"> (</span><img alt="" src="http://www.stocktrends.com/userfiles/image/ST-SolidStar2.gif" height="12" style="font-family: Arial; font-size: 15px; line-height: 1.38; white-space: pre-wrap;" width="13" /><span style="font-family: Arial; font-size: 15px; line-height: 1.38; white-space: pre-wrap;">). The market characteristic described by this Stock Trends indicator combination is of a stock that is relatively early in a Bullish trend but has tripped rather suddenly on significant bad market news. While the drop in price was substantial last week, TWTR is still only underperforming the S&P 500 by 5% measured over the past 13-weeks.</span></div>
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<span style="font-family: Arial; font-size: 15px; vertical-align: baseline; white-space: pre-wrap;">The <strong><a href="http://www.stocktrends.com/show_article.php?id=85">Stock Trends Inference Model</a></strong> (STIM) analysis is designed to make a statistical evaluation of market conditions - especially those market conditions that are most clearly defined. The Stock Trends Report on TWTR is now a good example. What does the STIM analysis say now about this stock’s future price expectations? Remember that the STIM analysis samples 30-years of Stock Trends data looking for stocks with similar indicator combinations, measuring post-observation statistics of 4-week, 13-week, and 40-week returns. From the samples we infer population parameters of these returns and estimate the probability of the current stock (here TWTR) bettering the estimated future returns of a randomly selected stock.</span></div>
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<span style="font-family: Arial; font-size: 15px; vertical-align: baseline; white-space: pre-wrap;">Here is the current STIM analysis of TWTR:</span></div>
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<span style="font-family: Arial; font-size: 15px; vertical-align: baseline; white-space: pre-wrap;">What does this analysis tell us? First, we see that the short-term price expectations are relatively neutral, with the mean return expectations near the expected return of a randomly selected stock (0%). There is a 51% probability that the 4-week return of TWTR will be positive (greater than 0%, the expected return of a broad market randomly selected stock). Not much better than the 50% probability that you would see a positive 4-week return in a randomly selected stock.</span></div>
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<span style="font-family: Arial; font-size: 15px; vertical-align: baseline; white-space: pre-wrap;">However, the 13-week and 40-week expected returns of TWTR are much more concerning. The probability of TWTR having a 13-week return better than the expected 13-week return of a randomly selected stock (2.19%) is only 45.2%. Looking further out on the time horizon is even more bleak. The probability of TWTR having a 40-week return greater than the expected 40-week return of a randomly selected stock (6.45%) is just 28.8%. Remember, a randomly selected stock has a 50% probability of having a 40-week return greater than 6.45%.</span></div>
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<span style="font-family: Arial; font-size: 15px; vertical-align: baseline; white-space: pre-wrap;">The STIM analysis tell us that TWTR, as defined by the current Stock Trends indicator combination, has a significantly low probability of delivering positive returns over the intermediate time periods ahead.</span></div>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-84752575902801933462015-04-15T09:31:00.000-04:002015-04-15T09:31:12.983-04:00Introducing the 'Map of Stock Trends'<div dir="ltr">
The Stock Trends Inference Model is a quantitative approach to interpreting the categorical data that is the core value-added analysis presented here. The Stock Trends indicators are derived from base tenets of the market technician’s encyclopedia - a toolset designed to reduce a complex market dynamic to a categorical, and hierarchical framework. By evaluating the statistical significance of this framework we can apply meaningful algorithmic trading methods.</div>
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However, the first step is to understand the data and interpret the Stock Trends Inference Model results. Every week we sample 30-years of data to assign a probability for future returns on over 7,000 North American stocks. Using combinations of categorical data and making assumptions about the distribution of returns, we apply statistical inference methods to differentiate stocks (ETFs and income trusts, too) by the estimated returns in the coming periods (4-weeks, 13-weeks, and 40-weeks). You can see the result of that analysis in the Profile section of each Stock Trends Report.</div>
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I’ve already introduced the Stock Trends Inference Model in previous editorials. Subscribers to Stock Trends Weekly Reporter can interpret this information weekly, as well as review the reports on issues with the best expected returns. The Stock Trends ‘Select’ report, as well as the Top 4-wk/13-wk/40-wk returns expectations reports give users a new way to make the Stock Trends reports actionable.</div>
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However, these reports can be augmented by data visualizations. Graphical presentations of data are always useful in translating vast data points into more accessible interpretations. A good graph saves us time and points us in the right direction.</div>
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The Stock Trends Profile reports include heatmaps which help us compare returns expectations among industry group member stocks. Another useful display method for this data, especially when we want to broaden the use of the data hierarchy, is a treemap. A treemap is specifically designed for hierarchical data and is commonly used. A popular example in our equity analysis space is the <a href="http://www.marketwatch.com/tools/stockresearch/marketmap">Map of the Market</a>.</div>
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Today I am introducing a treemap of the Stock Trends Inference Model - the <em>Map of Stock Trends</em>. It takes the data results from the weekly analysis, sorting 4-week and 13-week returns expectations by trend category.</div>
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In the treemaps displayed below large capitalization stocks (U.S. stocks with a market cap greater than $1-billion, Canadian stocks with market cap greater than $500-million) are grouped by Stock Trends indicator (Bullish <img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />, Weak Bullish <img alt="" height="14" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" width="16" />, Bearish <img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />, Weak Bearish <img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />, Bullish Crossover <img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />, Bearish Crossover <img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" width="12" />). Each stock within these groups are visually differentiated in two ways: spatially by their relative probability of a return greater than the base 13-week mean random return (2.19%) , with larger cells (higher probabilities) sorted and displayed from the upper left quadrant and moving down to the lower right corner for the lower value. Secondly, the 4-week returns expectations are differentiated visually by color gradation, with darker green hues representing stocks with higher probabilities of exceeding the base average 4-week random return (0%) and darker red hues representing the stocks with the poorest probabiltity of a positive return in 4-weeks.</div>
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Dark green cells in the upper left of each trend category are stocks with the best statistical trend characteristics. Dark red cells in the lower right quadrant of each trend category are stocks with the worst statistical trend characteristics.</div>
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Below are the current <em>Map of Stock Trends</em> treemaps for the U.S. and Canadian stock markets. Each Stock Trends trend indicator category grouping is identified by the translucent indicators in the background of each box. In the future the treemap will be developed in an application that allows users to click on an individual cell and go directly to individual Stock Trends Reports, but for now the visualizations help direct us to the stocks with the most favourable current Stock Trends Reports.</div>
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U.S. stock exchanges - big cap stocks</h3>
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Toronto Stock Exchange - big cap stocks</h3>
<img align="middle" alt="Map of Stock Trends" height="480" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20150514/20150410_T_Treemap.png" width="900" />Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-53492397890403451582015-03-05T11:25:00.000-05:002015-03-05T11:27:16.212-05:00Industry return expectations<div dir="ltr" style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
Wondering which U.S. sectors and industry groups are signalling the best opportunities for returns in the period ahead? The Stock Trends Inference Model presents a quantitative look at period returns for individual stocks, and from those return expectations the sector and industry group average return expectations can be measured.</div>
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Recall that the <a href="http://www.stocktrends.com/show_article.php?id=85">Stock Trends Inference Model</a> estimates the returns expectations for a stock, ETF or income trust given its current Stock Trends indicators. It does this by sampling for similar combinations of Stock Trends indicators over the past 30-years and measures post-observation price performance. From these samples statistical inference methodology is applied to estimate population mean and standard deviation parameters.</div>
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Every week over 6,000 issues have a Stock Trends indicator combination that has a minimum of 50 similar combinations in the data history, and you can find the resultant probability analysis in the Profile tab of these individual Stock Trends Reports. For instance, the current Stock Trends Profile of Solar Capital Ltd. (<a href="http://www.stocktrends.com/streport.php?symbol=SLRC-Q&s=3&p=6">SLRC</a>) shows that the expected 4-week return will be 5.6% and that the probability of a return greater than the base 4-week return expectation (which is 0%) is 62%. Our base expectation is that a stock has a 50% chance of a positive return in a 4-week period, so SLRC has a better chance of performing well, and is the top Nasdaq ST-IM Select stock this week.</div>
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The current week reports on 6,261 listings that have ST-IM returns estimations for 4-week, 13-week, and 40-week periods ahead. Breaking down those listings by sector and industry group gives us a better understanding of market timing trade opportunities. The heatmaps below rank sectors and industry groups by mean relative expectations over the three different time periods.</div>
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U.S sectors - ranking of return(%) expectations</h3>
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Currently, the top returns expectations are found in utilities, healthcare, and technology sectors. Conglomerates, Financials, and Industrial sectors have the worst returns expectations.</div>
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Each sector breaks down into industry groups. The following heatmap shows how the returns expectations for these groups rank.</div>
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U.S industry groups - ranking of return(%) expectations</h3>
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The industry groups with the best returns expectations, as averaged over the three periods, include utilities, consumer durables, and drug stocks. Financial services, conglomerates, and aerospace/defense stocks have the worst returns expectations.</div>
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The weekly Stock Trends ST-IM Select report shows the issues (stocks, ETFs, income units) with the best returns expectations over 13-weeks where the returns expectations are better than the base period returns expectations in all three periods (4-week, 13-week, and 40-week). [For rankings of return expectations within each period see the reports Top 4-week returns(%) expectations, Top 13-week returns(%) expectations, Top 40-week returns(%) expectations in the <a href="http://www.stocktrends.com/st_stonline_filters.php?exchange=N">ST Filters reports section</a>.]</div>
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Among the top ranked issues in the February 27th NYSE ST-IM report is the iShares U.S. Utilities ETF (<a href="http://www.stocktrends.com/streport.php?symbol=IDU-N&s=3&p=6">IDU</a>). Here Profile report shows that IDU has a 59% probability of beating the base period random return for each of the three periods. Recall that a stock chosen at random has a 50% chance of beating the broad market’s base period random return (i.e. a 0% return over 4-weeks, a 2.19% return over 13-weeks, and a 6.45% return over 40-weeks). With the given <a href="http://www.stocktrends.com/show_article.php?id=78">assumption of randomness</a>in market returns, a 59% probability of beating a random return constitutes an appreciable edge.</div>
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The heatmaps below rank the current returns expectations of large cap stocks represented in the Dow Jones Industrials index and the S&P/TSX 60 index. Microsoft (<a href="http://www.stocktrends.com/streport.php?symbol=MSFT-Q&s=3&p=6">MSFT</a>), Disney (<a href="http://www.stocktrends.com/streport.php?symbol=DIS-N&s=3&p=6">DIS</a>) and 3-M (<a href="http://www.stocktrends.com/streport.php?symbol=MMM-N&s=3&p=6">MMM</a>) top the DJI rankings, while Shaw Communications (<a href="http://www.stocktrends.com/streport.php?symbol=SJR.B-T&s=3&p=6">SJR.B</a>), Agnico Eagle Mines (<a href="http://www.stocktrends.com/streport.php?symbol=AEM-T&s=3&p=6">AEM</a>), and Blackberry (<a href="http://www.stocktrends.com/streport.php?symbol=BB-T&s=3&p=6">BB</a>) have the best blue chip Canadian stocks return expectations. You can view the Profile report of each of these and all stocks on the Stock Trends Report page.</div>
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Dow Jones Industrials stocks - ranking of return(%) expectations</h3>
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S&P/TSX 60 stocks - ranking of return(%) expectations</h3>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-83050566743268663562014-10-26T14:00:00.000-04:002014-10-26T14:00:08.383-04:00Bearish sentiment builds<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
Investors are always looking for the door. Even when returns are abundant and investor sentiment is wildly bullish, shareholders know that plump investment accounts are but paper profits - only real when the trigger is pulled and equity is once again cash. The degree to which investors look more nervously to the exit is proportional to the degree to which their equity positions are compromised. How we measure that compromise helps us identify critical shifts in investor sentiment and recognize high-risk market periods.</div>
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<br />Stock Trends is by design a categorical reporting framework that gives us a measure of aggregate investor sentiment and a metric for determining when market participants are feeling the squeeze and most ready to dash for the cash. The Stock Trends Bull/Bear Ratio is now serving notice that the exit doors are wide open.</div>
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<br />Most market analysts look at benchmark indexes of price level, pointing to areas of support and resistance to anticipate market rallies and corrections. Certainly, the 6% drop in the S&P 500 index from the market high in September sounded alarm bells. But we have had corrections of 5% and more multiple times during the bull market run since 2009. Can we expect this is just another typical and expected correction that will soon be subdued? Price level analysis can conjecture about that, but a measure of market breadth is the best barometer of how sentiment has truly shifted.</div>
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<br />The Stock Trends Bull/Bear Ratio measures the distribution of Stock Trends trend categories and tells us something quite simple: are the majority of stocks trending positively or negatively? Are the diversified holdings of investors buoyed by a rising tide or sinking in aggregate?</div>
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<br />The Stock Trends trend indicators categorize individual trends by the conditions of a simple moving average study. The base categories -<a href="http://www.stocktrends.com/main.php?page=stBullishref.php">Bullish</a> or <a href="http://www.stocktrends.com/main.php?page=stBearishref.php">Bearish</a> - are determined by the relationship of the 13-week and 40-week moving averages of price. If the 13-week average price is above the 40-week average price the stock is categorized as Bullish. If it is below the stock is categorized as Bearish. This is a factual reporting of past price performance.</div>
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<br />The price smoothing aspect of average prices gives us a clearer idea of trends, and although these longer-term time parameters are lagging in nature, they do make it possible to characterize long-term price movement. It is this long-term price movement that most shifts the balance of investor sentiment and creates heightened periods of anxiety about equities.</div>
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<br />Stock Trends tabulates the Bull/Bear Ratio for individual North American exchanges. The <a href="http://www.stocktrends.com/st_stsummary.php?x=N">New York Stock Exchange Bull/Bear Ratio</a> has been plummeting since August, and is now at 1.07. The <a href="http://www.stocktrends.com/st_stsummary.php?x=Q">Nasdaq Bull/Bear Ratio</a> dropped below 1.0 in June and is now at 0.66. When we look at the composite of both major exchanges - some 5,660 common stocks that currently have Stock Trends trend indicators - we get a good look at the trend breadth of the U.S. stock market.</div>
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<br />The graph below highlights periods where the Stock Trends Bull/Bear Ratio for the combined Big Board and Nasdaq exchanges has been rated as 'Bullish'. These shaded areas tell us when investor sentiment provides more fertile ground for market rallies and rebounds. There will be times when the S&P 500 index rallies without broader market support , like in late 2006, but these can represent divergences between large cap and small cap performances. Generally, a strong bullish investor sentiment is characterized across the stock market. The Stock Trends Bull/Bear Ratio gives us that representation of market breadth.</div>
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<br />Where are we at now? The U.S. market Bull/Bear Ratio has been flirting with a Bearish investor sentiment reading since the market top in the summer, and has now dropped to 0.7. Canadian investors sentiment has also dipped into Bearish territory - the Stock Trends <a href="http://www.stocktrends.com/st_stsummary.php?x=T">TSX Bull/Bear Ratio</a> fell below 1.0 this week (now at 0.85).</div>
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Investors should take note that this aggregate North American trend condition makes the market vulnerable to a crash as investors increasingly weigh in about making an exit. The S&P 500 index's 4.1% recovery last week may be heartening, but fading investor sentiment should keep investors on high alert.</div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-77512167815545953482014-09-24T09:02:00.000-04:002014-09-24T09:02:57.421-04:00Stock Trends RSI +/- pattern analysis<table cellpadding="0" cellspacing="0" style="border: 0px solid rgb(153, 153, 153); vertical-align: top; width: 100%px;"><tbody>
<tr><td style="padding: 10px;" width="100%">Stock Trends Reports new Profile tab now also includes a pattern analysis of the RSI +/- indicator. This analysis looks to answer questions about a stock's volatility in particular price trends and how weekly price movement provides an indication of probable outcomes for the coming week.<br />
<br />The Stock Trends <a href="http://www.stocktrends.com/main.php?page=stguidersi.php">RSI +/- indicator</a> is a simple binary marker of weekly price performance relative to the benchmark market index. If a stock (all North American trading issues and indexes covered by the Stock Trends analysis) outperforms the benchmark (the S&P 500 for U.S. stocks, the S&P/TSX Composite index for Canadian stocks) the stock is assigned a (+). If it underperforms, it is assigned a (-).<br />
<br />This binary notation of price performance can be a useful framework for an event sample space and inference model. From this we can derive probabilities of certain outcomes and estimate one week returns (%).<br />
<br />Binary events are always interesting. They provide a simple modeled sample space of possible outcomes. The most common example is the flipping of a coin. We know when we flip a fair coin that there is a 50% chance that the outcome will be heads, and an equal 50% chance the outcome will be tails. How does this kind of random event compare to the binary RSI +/- event?<br />
<br />Indeed, there is no surprise when the Stock Trends data reveals that almost all stocks have a near-50% chance of turning up an RSI +/- on any given week. But that is for a sample space that includes all the data. For instance, for <a href="http://www.stocktrends.com/streport.php?symbol=IBM-N&s=3&p=6">IBM</a> the Stock Trends weekly data shows that of the 1,800 weeks covered, 49.5% of observations show an RSI (+) as the weekly indicator. Although some stocks like <a href="http://www.stocktrends.com/streport.php?symbol=INTC-Q&s=3&p=6">INTC</a> show a 51.6% probability of a (+) over their history, the mean value across all stocks tends to 50%.<br />
<br />A question that comes from this random-like event becomes quite apparent: how does this probability change under different market characteristics? For example, if a stock is in a Stock Trends Bullish trend, what is the probability of an RSI (+) indicator? We can also ask what is the probability we will see an RSI (+) in the upcoming week if the previous week was also a (+) while the stock is in a Bullish trend?<br />
<br />Using the samples of the stock's data history that match certain patterns of market performance and underperformance we can also derive similar probability statements. Although this analysis operates under the assumption of randomness in market returns, we are looking at the pattern of past performance and estimate the probability of an outcome derived from the event sample space.<br />
<br />In short, like a gambler looking for evidence of an 'unfair' coin that can be capitalized on, we are looking for evidence of a pattern that provides us with better probabilities of a desired outcome than the base probability - which is 50%.<br />
<br />Introduced in <a href="http://www.stocktrends.com/show_article.php?id=85">last week's editorial</a>, the Stock Trends Reports Profile section is the first element of the Stock Trends Inference Model - the implied population parameters and distribution of like Stock Trends indicator combinations. Also presented was a heatmap that ranks the estimated returns of stocks in an industry group. These elements of the inference model focus on homogeneous patterns across markets and estimates 4-week, 13-week, and 40-week returns for individual stocks. The RSI +/- pattern analysis differs by focusing on patterns with the stock itself, estimating returns based on these internal samples.<br />
<br />Last week <a href="http://www.stocktrends.com/streport.php?symbol=AAPL-Q&s=3&p=6">AAPL</a> was presented as an example, so we'll use it again for illustrating the RSI +/- pattern analysis. Below we can see the most recent history of the weekly Stock Trends indicators for AAPL.<br />
<img alt="" height="505" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140921_AAPL_history.png" width="640" /><br />
The current RSI +/- indicator is (-). In this analysis two categorical variables - the Stock Trends trend indicator and the RSI +/- indicator - are inputs. The output is the returns, or percentage change in price, in the week following the observation. What kind of returns (%) do the data show after the observation of a particular pattern of RSI +/- indicators when a stock is labeled in a specific trend?<br />
<br />Here is the current RSI +/- pattern analysis of AAPL:<br />
<img alt="" height="1778" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140921_AAPL_pattern.png" width="560" /><br />
To repeat, only weeks of the AAPL data showing the same trend indicator as the current trend indicator (strong Bullish) of AAPL are evaluated. Here we are looking to find how the binary RSI +/- probabilities differs from the probabilities we already understand about the aggregate for this and most stocks - a near 50% chance of either a (+) or a (-).<br />
<br />Is the coin somehow biased in a particular trend? If so, to what degree? In this case, given the current RSI +/- patterns for AAPL, what does the data history tell us about how the stock performed subsequently to these patterns when the stock was in the same Bullish trend?<br />
<br />The length of the longest pattern of RSI +/- indicators for each stock analyzed depends on the data available. Here the longest pattern measured is 6-weeks long. However, practical usage of the analysis probably lends itself best to periods of three or four weeks.<br />
<br />In any event, the probabilities for binary outcomes as the pattern extends is of interest in evaluating the quality of the probabilities of the shorter term patterns. In the current AAPL example, the patterns all suggest that the current market underperformance indicated by the (-) will most probably be followed by a market outperformance (+).<br />
<br />Of course, with a given probability of market outperformance we would like to know the returns expectations. If AAPL does outperform the market next week, what is the expected change in price in the coming week? The analysis above defines intervals for the returns expectations for AAPL for each length of the pattern - here from one to six weeks.<br />
<br />This type of short-term price movement analysis can be used in tandem with the longer-term analysis provided by the Stock Trends Inference Model and detailed above the RSI +/- Pattern Analysis on the Profile tab of each Stock Trends Report. It can also be profitably used in short-term options trade setups, something Stock Trends will be able to advise about in the future.</td></tr>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-45124593078328052002014-09-15T20:06:00.000-04:002014-09-15T20:06:43.656-04:00The new Stock Trends Report Profile section<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<span style="color: windowtext; font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">A much longer time in coming than originally planned, the new Stock Trends Report 'Profile' section is </span><span style="color: windowtext; font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">installed </span><span style="color: windowtext; font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">on the Stock Trends website. Available for most common stocks and exchange traded funds, the Profile </span><span style="color: windowtext; font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">report </span><span style="color: windowtext; font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">now features the Stock Trends Inference Model introduced in the past year. This analysis attempts to answer the very important question: what return expectations do the Stock Trends Reports imply?</span><span style="color: windowtext; font-size: 6pt;"> </span></div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">If you were lucky (?) enough to take statistics in your previous studies, you are probably reasonably versed in </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">statistical inference methodology. You will know about measurements of central ten</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">dency and varia</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">bility</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">. You will know about the 'mean' and 'standard deviation' - both as </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">descriptive </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">statistics of a sample and </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">estimated </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">parameters of a</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> population. And you will also know about sample spaces and probabilities. The Stock Trends Inference Model is an application of </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">these </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">basic statistical methods.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">If you were lucky enough to have avoided a statistics course in school, be assured th</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">is model can be explained in very simple and cle</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">ar language. I've tried to do that in the editorials I have </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">al</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">ready</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> written about this analysis, but let's summarize here.</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> First, though, </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">a description of methodology </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> should always be preceded by a sta</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">tement of the research question and the biases of that question.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">Because t</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">he departure point for any model is the assumptions that underlie </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">it</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">,</span> <span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">i</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">t's important to fully understand the fundamental premises of the Stock Trends Inference Model.</span> <span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">The primary assumption is a core tenet of technical analysis - that pric</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">e patterns repeat themselves. </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">In order to illustrate this and display </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">empirical observations</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> of patterns</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> m</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">arket technicians </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">must </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">assert that </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">these </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">market price and volume patterns are homogeneous across markets.</span></div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">What does that mean? It means that a pric</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">e pattern observed in one market can </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">be meaningfully</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> applied to another market. A moving average crossover, for example, carries significance as much in AAPL as it does in ZNGA. A head-and-shoulders pattern found in the chart of IBM in 1980 a template for one in BAC in 2010</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> (please note: not factual dates for this </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">example)</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">If t</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">he </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">application of technical patterns depends upon the premise that these patterns repeat themselves</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">, w</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">hat use is the observation of a</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">n historical</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> pattern if it does not offer some predicative accuracy? </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">It is not enough to be doctrinaire in our answer and provide anecdotal evidence of positive outcomes. We must provide a larger number of outcomes as </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">evidence.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">Of course, no matter how large </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">the </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">sample size of the</span> <span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">outcomes we present, the evidence will always be just a portion of the total number of possible outcomes across markets and across time. When we loo</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">k at possible outcomes we must understand that these outcomes are not based on the market conditions of a particular moment. That would be unsatisfactory and biased. Because we cannot possibly know what will happen in any particular market, we must instead look at the estimating the character of all markets in a given condition.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">In the Stock Trend</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">s Inference Model the given condition is represented by the Stock Trends indicator combination. Th</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">at</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> combination is the </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">aggregate of the </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">Stock Trends trend indicator, the length of time the current trend category (<a href="http://www.stocktrends.com/main.php?page=stBullishref.php">BULLISH </a>or <a href="http://www.stocktrends.com/main.php?page=stBearishref.php">BEARISH</a>), the <a href="http://www.stocktrends.com/main.php?page=stTrendCnt.php">length of time</a> of the current trend indicator, the 13-</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">week <a href="http://www.stocktrends.com/main.php?page=stguidersi.php">Relative Strength (RSI)</a> indicator value, the 1-week RSI +/- indicator, and the <a href="http://www.stocktrends.com/main.php?page=stguideVolume.php">volume indicator</a>. These indicators quantify and categorize </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">a </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">market condition in terms of trend and price momentum.</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> Distinct c</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">ombinations of these indicators qualify particular trends by price momentum and volume characteristics.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">If we look at the current Stock Trends indicator combination of <a href="http://www.stocktrends.com/streport.php?symbol=AAPL-Q&s=3&p=6">AAPL</a>, for example, we can see that this stock is in the 31st week of being labeled with a (strong) Bullish indicator and that it has been in a BULLISH trend category for 52-weeks. Its 13-week Relative Strength i</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">ndicator (RSI) value is 109, indicating AAPL has outperformed the S&P 500 index by approximately 9% over the past 13-week period. The current RSI +/- indicator is (+)</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> , indicating the stock outperformed the benchmark market index in the past week. Finally, there is no Unusual Volume indicator, indicating that last week's trading volume was not high or low enough to be assigned either a high or low volume indicator.</span> </div>
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<img alt="" height="424" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140915_AAPL_STReport.png" width="640" /></div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">Taken as a composite, these indicators tell us that AAPL is in a re</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">latively</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> solid long-term trend. Given these characteristics of a stock's trend and its length of trend, as well as its pric</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">e momentum, what does this particular categorization imply about future pric</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">e mo</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">vement</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">?</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">Of course, we cannot precisely know what is to happen in the future. All we can do is look upon what has happened in the past and make some kind of estimation of what will happen in the future. Using statistical methods we can translate past observations of what has happened into a probability statement about what will happen in the future. The Stock Trend</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">s Inference Model attempts to do this.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">Below is the current Stock Trends Inference Model report found under the Profile tab of <a href="http://www.stocktrends.com/streport.php?symbol=AAPL-Q&s=3&p=6">AAPL-Q</a>.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">The first section shows the Sample distribution plot and estimated returns distribution for three different periods - 4-week, 13-week, and 40-week. In this case the sample - derived from the 30-year history of Stock Tren</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">ds data - is </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">616 records of stocks which sported similar </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">Stock Trends Report indicator combinations to the current indicator combination of AAPL. From this sample we are measuring the subsequent price performance over the three different periods.</span> </div>
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<img alt="Return(%) expectations for Apple $AAPL" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140915_AAPL_STIM.png" /></div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">Here we ask of this sample: what kind of returns (%) did other stocks have which previously exhibited similar trend and momentum characteristics as defined by the Stock Trends indicator combination?</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">The green density plot for each of the three periods is displayed. Each shows how the returns were distributed. This plot shows where most of the returns tended toward (central tendency) as well as the variability of the returns (variance). We ar</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">e interested in measuring central ten</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">dency</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> and variance of the sample because with those statistics we can estimate the average return and variance of the population of all returns associated with this Stock Trend</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">s indicator combination. Remember, the population of all returns is much large than this sample size - it includes returns not in this database. It includes future returns - the returns investors ar</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">e most interested in!</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">Using statistical inference methods we can estimate the mean of a population within a certain range, or interval, and we can be certain of that interval to a defined degree. Here our model is 95% certain of</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> the mean intervals. Since we are most concer</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">ned about the lowest estimate of the interval, we know that there is only a 2.5% chance that the mean of the population is less than the low end of the interval.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">For the investor it is </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">more meaningful to interpret the mean interval of the population as the estimated return of a portfolio of stocks that have the same Stock Trends indicator combination.</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> F</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">rom this theor</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">etical</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> portfolio we can make another important assumption: that returns of randomly chosen samples</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">, when estimated as the portfolio population, will have a normal, bell-shaped distribution. This assumption is an extension of a well-understood probability theory rule</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">: the central limit theorem.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">This assumption - and evidence - of randomness in market returns provides us with a very useful framework for making probability statements about the expected return of a stock. With an estimated population mean, an estimated standard deviation of the distributions we can derive probability statements about observing a return above specific values from a normally distributed sample space.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">In short, the Stock Trend</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">s Inference Model translates our samples into an estimated return and gives us the </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">probability that a return will</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> better</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> a given, benchmark return.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">What should that benchmark return be? It's not difficult to see that the base return we should measure against is the return of a randomly selected stock. If we are measuring returns based on the rand</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">omness of outcomes of a categorized sample (our Stock Trend</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">s indicator combinations), the base return should be the return we would expect if we randomly picked a stock across the broad market</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">. Indeed, any trading system results should be measured against the results of a randomly selected portfolio.</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> If you can't beat the monkey, why bother?</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">The base period random return benchmarks are as follows: 4-week (0%), 13-week (2.19%), and 40-week (6.45%). Each of these returns are the return means</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> of over 500,000 samples taken at random over a 30-year period. Not surprisingly, the annualized return of randomly selected stocks is basically equivalent to</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> long-term market returns - 8%. This sobering fact</span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"> should remind </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">market timing traders that no matter what analytical framework used, the returns generated by a </span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">buy-and-hold approach must be discounted, regardless of what the market actually provided during a particular period.</span><span style="color: windowtext; font-size: 6pt;"> </span></div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US">I'll be looking at Stock Trends Inference Model analysis in the future, and pointing out ways to turn this analysis into profitable managed trades. Also, I'll be introducing an additional new analysis under the Profile section. It is a pattern recognition analysis that also employs statistical inference. Expect this content addition very soon.</span> </div>
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<span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"><span style="margin: 0px; padding: 0px;">Another recent content addition to the Stock Trends Reports on the website is the charting application. This is a third-party application provided by </span><span style="background-image: url(data:image/gif; background-position: 0% 100%; background-repeat: repeat-x; border-bottom-color: transparent; border-bottom-style: solid; border-bottom-width: 1px; margin: 0px; padding: 0px;"><a href="https://www.tradingview.com/">Tradingview</a></span><span style="margin: 0px; padding: 0px;">. For subscribers </span><span style="margin: 0px; padding: 0px;">interested in marking up a chart of a given stock, Try it out! It also provides additional content including current intra-day pricing (15-minute delay), recent news headlines, </span></span><span style="font-size: 11pt; line-height: 20px; margin: 0px; padding: 0px;" xml:lang="EN-US"><span style="margin: 0px; padding: 0px;">social media comments from </span><span style="background-image: url(data:image/gif; background-position: 0% 100%; background-repeat: repeat-x; border-bottom-color: transparent; border-bottom-style: solid; border-bottom-width: 1px; margin: 0px; padding: 0px;">StockTwits</span><span style="margin: 0px; padding: 0px;">, as well as technical and fundamental data.</span></span> </div>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-9070178135444831862014-05-05T16:31:00.000-04:002014-05-05T16:31:22.963-04:00Emerging markets invitation<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
Emerging market stocks are earning back investor confidence. When the 'risk-on' button has been pushed market-timing traders are prepared to rotate into foreign equities at the earliest sign of sustained price momentum. Considering the muted volatility of the broad North American stock market, evidence of a blooming sector rotation to overseas equity risk is a welcome technical signal for investors.</div>
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In U.S. dollar terms, <a href="http://www.stocktrends.com/st_indxgrp.php?indexgrp=D">Brazilian equities</a> are outperforming U.S. equities by 13 per cent since the end of January. Southeast Asian markets - Indonesia, Thailand, and Philippines in particular - are also setting a much better pace than U.S. stocks. These markets are currently labeled as Stock Trends Weak Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />) - indicating that the primary long-term bearish trend category is shifting - or have recently had a Bullish Crossover (<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />) and changed to a Stock Trends Bullish (<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />) trend. Many have rallied nicely in the past month. It's not surprising to see a surge in the number of stock picks that fit the emerging market theme.</div>
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The most recent Stock Trends <strong>Picks of the Week</strong> report features over twenty emerging market stocks and ETFs. Bank, communications, utilities, and energy stocks lead this group.</div>
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<br /><br />Emerging market stocks and ETFs on the move</h3>
<table align="center"><tbody>
<tr bgcolor="#009900" valign="baseline"><th><h5>
Trend</h5>
</th><th><h5>
RSI</h5>
</th><th><h5>
Issue</h5>
</th><th><h5>
Price($)</h5>
</th><th><h5>
wk%chg</h5>
</th><th><h5>
Vol(00s)</h5>
</th><th><h5>
</h5>
</th></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" /></td><td align="left">138</td><td><a href="http://www.stocktrends.com/streport.php?symbol=BBD-N&s=3&p=6">Banco Bradesco SA (BBD)</a></td><td><div align="right">
15.30</div>
</td><td><div align="right">
2.8</div>
</td><td><div align="right">
423586</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">121</td><td><a href="http://www.stocktrends.com/streport.php?symbol=BSAC-N&s=3&p=6">Banco Santiago - Chile (BSAC)</a></td><td><div align="right">
24.91</div>
</td><td><div align="right">
8.1</div>
</td><td><div align="right">
25892</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">122</td><td><a href="http://www.stocktrends.com/streport.php?symbol=CIB-N&s=3&p=6">Bancolombia S A (CIB)</a></td><td><div align="right">
56.79</div>
</td><td><div align="right">
2.6</div>
</td><td><div align="right">
20410</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">120</td><td><a href="http://www.stocktrends.com/streport.php?symbol=CBD-N&s=3&p=6">Comp. Brasil. de Distribuicao (CBD)</a></td><td><div align="right">
48.41</div>
</td><td><div align="right">
3.4</div>
</td><td><div align="right">
22919</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">120</td><td><a href="http://www.stocktrends.com/streport.php?symbol=CIG-N&s=3&p=6">Companhia Energetica (CIG)</a></td><td><div align="right">
7.31</div>
</td><td><div align="right">
2.2</div>
</td><td><div align="right">
249327</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">108</td><td><a href="http://www.stocktrends.com/streport.php?symbol=EOC-N&s=3&p=6">Empresa Nacional Electricidad (EOC)</a></td><td><div align="right">
43.82</div>
</td><td><div align="right">
3.1</div>
</td><td><div align="right">
5512</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">115</td><td><a href="http://www.stocktrends.com/streport.php?symbol=ENI-N&s=3&p=6">Enersis SA (ENI)</a></td><td><div align="right">
16.11</div>
</td><td><div align="right">
2.9</div>
</td><td><div align="right">
31405</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">103</td><td><a href="http://www.stocktrends.com/streport.php?symbol=IEMG-N&s=3&p=6">iShares Core MSCI Emerging Mkt (IEMG)</a></td><td><div align="right">
49.78</div>
</td><td><div align="right">
1.5</div>
</td><td><div align="right">
67434</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">101</td><td><a href="http://www.stocktrends.com/streport.php?symbol=IPFF-N&s=3&p=6">iShares Intl Preferred E.T.F. (IPFF)</a></td><td><div align="right">
24.82</div>
</td><td><div align="right">
2.8</div>
</td><td><div align="right">
1893</div>
</td><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidStar2.gif" /></td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">111</td><td><a href="http://www.stocktrends.com/streport.php?symbol=ILF-N&s=3&p=6">iShares Latin Amer 40 E.T.F. (ILF)</a></td><td><div align="right">
38.67</div>
</td><td><div align="right">
2.9</div>
</td><td><div align="right">
34038</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">117</td><td><a href="http://www.stocktrends.com/streport.php?symbol=EWZ-N&s=3&p=6">Ishares Msci Brazil Capped ETF (EWZ)</a></td><td><div align="right">
48.48</div>
</td><td><div align="right">
3.8</div>
</td><td><div align="right">
828316</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">103</td><td><a href="http://www.stocktrends.com/streport.php?symbol=EEM-N&s=3&p=6">iShares MSCI Emerg Mkt E.T.F. (EEM)</a></td><td><div align="right">
41.61</div>
</td><td><div align="right">
1.9</div>
</td><td><div align="right">
2784643</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" /></td><td align="left">102</td><td><a href="http://www.stocktrends.com/streport.php?symbol=EWM-N&s=3&p=6">iShares MSCI Malaysia E.T.F. (EWM)</a></td><td><div align="right">
15.84</div>
</td><td><div align="right">
1.5</div>
</td><td><div align="right">
71161</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">105</td><td><a href="http://www.stocktrends.com/streport.php?symbol=EWS-N&s=3&p=6">iShares MSCI Singapore E.T.F. (EWS)</a></td><td><div align="right">
13.59</div>
</td><td><div align="right">
1.7</div>
</td><td><div align="right">
67869</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">111</td><td><a href="http://www.stocktrends.com/streport.php?symbol=THD-N&s=3&p=6">iShares MSCI Thailand E.T.F. (THD)</a></td><td><div align="right">
76.34</div>
</td><td><div align="right">
1.7</div>
</td><td><div align="right">
12381</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">106</td><td><a href="http://www.stocktrends.com/streport.php?symbol=LFL-N&s=3&p=6">LATAM Airlines Group S.A. (LFL)</a></td><td><div align="right">
15.50</div>
</td><td><div align="right">
3.3</div>
</td><td><div align="right">
26946</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" /></td><td align="left">103</td><td><a href="http://www.stocktrends.com/streport.php?symbol=MSD-N&s=3&p=6">Morg Stan Em Mk Debt (MSD)</a></td><td><div align="right">
10.15</div>
</td><td><div align="right">
0.5</div>
</td><td><div align="right">
2920</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" /></td><td align="left">115</td><td><a href="http://www.stocktrends.com/streport.php?symbol=IDX-N&s=3&p=6">Mrk Vectr Indonesia E.T.F. (IDX)</a></td><td><div align="right">
25.54</div>
</td><td><div align="right">
0.8</div>
</td><td><div align="right">
7854</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">125</td><td><a href="http://www.stocktrends.com/streport.php?symbol=ELP-N&s=3&p=6">Paranaense De Energia Copel (ELP)</a></td><td><div align="right">
15.08</div>
</td><td><div align="right">
7.3</div>
</td><td><div align="right">
19917</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">104</td><td><a href="http://www.stocktrends.com/streport.php?symbol=SCHE-N&s=3&p=6">SCHWAB EMG MKT ETF (SCHE)</a></td><td><div align="right">
24.70</div>
</td><td><div align="right">
1.5</div>
</td><td><div align="right">
11655</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" /></td><td align="left">102</td><td><a href="http://www.stocktrends.com/streport.php?symbol=RWX-N&s=3&p=6">SPDR DJ Int Real Estate E.T.F. (RWX)</a></td><td><div align="right">
42.70</div>
</td><td><div align="right">
1.1</div>
</td><td><div align="right">
13119</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">129</td><td><a href="http://www.stocktrends.com/streport.php?symbol=TEO-N&s=3&p=6">Telecom Argentina (TEO)</a></td><td><div align="right">
20.82</div>
</td><td><div align="right">
7.3</div>
</td><td><div align="right">
7144</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">107</td><td><a href="http://www.stocktrends.com/streport.php?symbol=VIV-N&s=3&p=6">Telefonica Brasil (VIV)</a></td><td><div align="right">
21.41</div>
</td><td><div align="right">
4.6</div>
</td><td><div align="right">
88363</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">104</td><td><a href="http://www.stocktrends.com/streport.php?symbol=TEI-N&s=3&p=6">Templeton Emg Mkt Incm Fd (TEI)</a></td><td><div align="right">
14.50</div>
</td><td><div align="right">
0.9</div>
</td><td><div align="right">
6251</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">110</td><td><a href="http://www.stocktrends.com/streport.php?symbol=UGP-N&s=3&p=6">Ultrapar Participacoes Sa (UGP)</a></td><td><div align="right">
25.71</div>
</td><td><div align="right">
3.1</div>
</td><td><div align="right">
12621</div>
</td><td> </td></tr>
<tr><td align="center"><img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" /></td><td align="left">104</td><td><a href="http://www.stocktrends.com/streport.php?symbol=VWO-N&s=3&p=6">Vanguard FTSE Emerging Markets (VWO)</a></td><td><div align="right">
41.24</div>
</td><td><div align="right">
1.9</div>
</td><td><div align="right">
622375</div>
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<em><span style="font-size: smaller;">The Relative Strength Indicator (RSI) provides a measure of the strength of a stock’s 13-week price movement relative to the S&P 500 index. Stocks outperforming the index have an RSI value above 100.</span></em></div>
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But should investors be jumping on these stocks now? Timing is everything for active traders, but is now the time for investors with a longer trade horizon? For the technician evaluating a <a href="http://www.stocktrends.com/stchartData.php?symbol=EEM-N&p=5y&page=stchart&Go=Go">multi-year chart</a>of the iShares MSCI Emerging Markets ETF (<a href="http://www.stocktrends.com/streport.php?symbol=EEM-N&s=3&p=6">EEM-N</a>), for instance, the answer to the question would come from further price development above a long-term price formation that indicates consolidation. Even the impressive spring rally that has propelled Brazilian stocks is not convincing enough when measured against the backdrop of a bearish long-term trend. How can we know if the rewards will match the risk inherent in these markets?</div>
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Stock Trends indicators report on price trends, but they also give guidance on what kind of future returns those trend categories imply. Sampling indicator combinations like the ones currently sported by these emerging market issues and statistically measuring post-observation returns gives us a more quantitative interpretation of this group of performing stocks and ETFs.</div>
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For example, the sample of 145 stocks that have had similar Stock Trends indicator combination and trend longevity as the iShares MSCI Brazil Capped ETF (<a href="http://www.stocktrends.com/streport.php?symbol=EWZ-N&s=3&p=6">EWZ-N</a>) gives us an expected return below the expected market return for the coming 4-week, 13-week and 40-week time periods. The graph below shows the distribution of 13-week returns of the sample and the assumed normal distribution of the population of stocks with similar Stock Trends indicators.</div>
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<img alt="" height="461" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140505_EMZ.png" width="650" /></div>
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<em><span style="font-size: smaller;">The sample density distribution is filled in green. The assumed population distribution - a normal distribution - is outlined in blue. The vertical yellow line indicates the estimated population mean return. The vertical red line indicates the base return of a randomly selected stock.</span></em></div>
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The following heatmap graph ranks some of the issues highlighted here by estimated returns implied in the market conditions categorized by the Stock Trends indicators. The estimated returns (%) of 4-week, 13-week, and 40-week periods are discounted by the base period expected market returns. The green elements of the map represent progressively higher performance expectations. Yellow elements represent market performance, while red elements represent progressively lower performance expectations.</div>
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Ranking of returns expectations: 4-week, 13-week, 40-week</h3>
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<img alt="" height="545" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140505_editorial.png" width="650" /></div>
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What does this tell us? The statistical inference analysis doesn't give the investor much confidence about returns over the next three quarters for many of these issues. This is not to say that these emerging market issues are not now in nascent stages of bullish trends, but the markers of more promising return expectations in the immediate future are not yet in place. For more conservative investors there is no need to jump on these risky markets yet. </div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-5056407825877974122014-04-29T17:03:00.000-04:002014-04-29T17:03:52.058-04:00Data-driven technical analysis<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
The stock market is a great laboratory. Considering the immense scope of data fueling asset valuations and ultimately influencing market price behaviour, it's not surprising that quantitative models are increasingly used to harness this data. Where analysis frameworks formerly tended to be doctrinaire - whether fundamental or technical - data science is now interjecting a new standard. Data-driven analysis is a booming business.</div>
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<br />For technical analysts the rigours of data science present a challenge. The foundation of technical analysis is clearly stated in its primary tenets: the market is transparent, prices trend, and move in identifiable patterns that repeat themselves. The chartist is a practitioner of pattern recognition. But how well do these patterns hold up to data science methods? Does the data support the chart patterns and indicators that are the bread and butter of market technicians?</div>
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<br />Although many very successful traders have made their fortunes and fame out of technical analysis, skeptics of the profession have always weighed in. And rightly so. Even market technicians self-proclaim their craft as equal parts science and art. However, those two endeavours don't often mingle well. Science is far too precise to indulge anecdote or flourishes of doctrine unsupported by the cold currency of hard evidence. Art is often too subjective or personal to codify. But quantitative analysis demands codification and measurement of variables.</div>
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<br />There is no shortage of technical analysts peddling doctrinaire assertions. Typically, almost every chart pattern presented lacks supporting quantitative evidence of predicative value. The language of the market technician largely fixates on what amounts to textbook, anecdotal guidelines. When assertions about probable outcomes are ventured, seldom do statistical measures accompany them. A recent article published by a technical analyst, for instance, said the following:</div>
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<br /><em>"The highest probability setups are the ones that have all the key moving averages on the right side of them. That doesn't mean that other setups will not work, it just means that the odds are slightly higher when this does occur." See '<a href="http://realmoney.thestreet.com/articles/04/25/2014/i-pullbacks-vipshop">I like pullbacks on Vipshop'</a></em></div>
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<br />Here the use of the word 'probability' implies some kind of definition of a sample space and its measurement. Unfortunately, most technicians offer neither. Statements like the above are bandied about as doctrine, but have no data to back them up. For the data scientist this is verboten.</div>
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<br />We now live in a world where data can give us the answers we need, and whether we like the answers or not we must let the data confirm or refute our hypothesis about relationships between variables - or even prove causation if necessary. If you are serious about technical analysis it is important to learn the language and process of data science.</div>
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<br />In an effort to address these higher standards I have started to model Stock Trends in the garb of quantitative data analysis. The Stock Trends indicators translate weekly market data into categories, giving the investor a quick and effective way to put current North American stock prices into a trend context. This categorical data fits into a number of data science approaches that transform the Stock Trends indicators into simple statistical models.</div>
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<br />This is now an important departure point for any technical methodology - how does the data support an analysis framework? In this case, do the Stock Trends indicators tell us something meaningful about future share price movement? For instance, how meaningful is a Stock Trends Bullish Crossover, alternatively referred to as a Golden Crossover in the lexicon of technical analysts?</div>
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<br />The Stock Trends indicator combinations provide an effective data foundation for a statistical inference model. Every week traded issues on the major North American exchanges are codified by these indicator combinations. As an example, last week the Stock Trends indicator combination for Fedex (<a href="http://www.stocktrends.com/streport.php?symbol=FDX-N&s=3&p=6">FDX-N</a>) was represented in the Stock Trends Report:</div>
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<img alt="" height="390" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140429_FDX.png" style="background-color: white; font-family: Arial, Helvetica, sans-serif;" width="650" /><span style="background-color: white; font-family: Arial, Helvetica, sans-serif;"></span><br />
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<br />Fedex's stock is labeled as Stock Trends (strong) Bullish ( <img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />). It has been a (strong) <a href="http://www.stocktrends.com/main.php?page=stBullishref.php">Bullish </a>stock for 8-weeks, and has been categorized in a Bullish trend for 71-weeks (see <a href="http://www.stocktrends.com/main.php?page=stTrendCnt.php">trend counters</a>). The stock has under-performed the S&P 500 index by 4% in the past 13-weeks, as indicated by the Stock Trends<a href="http://www.stocktrends.com/main.php?page=stguidersi.php">Relative Strength indicator </a>(96). Last week it also underperformed the benchmark index, as indicated by the RSI (-) sign. Finally, there is no <a href="http://www.stocktrends.com/main.php?page=stguideVolume.php">unusual volume indicator</a>, as defined by Stock Trends. This combination of Stock Trends indicators codifies market characteristics of Fedex's stock at this moment.</div>
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<br />What does this Stock Trends indicator combination tell us about future price movement? Can we assert some probability statement that is based on data evidence? If we want to generalize about a market condition like the one categorized by this Stock Trends indicator combination we must first make an<a href="http://www.stocktrends.com/show_article.php?id=80">assumption</a>: market conditions are non-specific to a security. This is an integral premise of technical analysis - that patterns evident in one security have relevance in patterns evident in another security.</div>
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<br />In order to assign probability statements a sample space of possible outcomes must be defined. We can estimate this sample space through statistical inference methods. In the case of the Stock Trends indicator combinations we can ask the question: how did other stocks with similar indicator combinations perform in the past?</div>
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<br />The answer to that question is found in the data. By extracting all like combinations in the 30-year data history we obtain a sample of stocks from which we can measure the post-observation returns. This statistic will measure the change in share price after 4-weeks, 13-weeks, and 40-weeks.</div>
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<br />The sample extracted from the data finds 91 other like observations - stocks that sported similar Stock Trends indicator combinations in the past. The distribution of returns for each of these periods is of interest, but here is the sample distribution of post-observation 13-week returns for stocks with similar Stock Trends indicator combinations as the current Stock Trends Report of Fedex.</div>
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<img alt="" height="436" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140429_FDX_13wk.png" width="650" /><br /><em>The sample density distribution is filled in green. The assumed population distribution - a normal distribution - is outlined in blue. The vertical yellow line indicates the estimated population mean return. The vertical red line indicates the base return of a randomly selected stock. </em><br /><br />Expected 13-week returns (%) implied by the Stock Trends Inference Model can be summarized briefly:<br /><br />For 13-week CLOSE returns estimation, with 95 % confidence, the 13-week CLOSE mean return of the population of stocks with a similar Stock Trends indicator combination to FDX will be inside [ 5.688 %, 10.137 %], with probability of 2.5 % we will have a mean return below 5.688%.</div>
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<br />The mean return 7.91% and standard deviation of 12.26% tell us that a normal distribution of 13-week CLOSE returns implies a probability of 67.97% that the expected return will be above the base 13-week random return of 2.19%.</div>
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<br />FDX is listed in the current Stock Trends Inference Model (ST-IM) Select stocks <a href="http://www.stocktrends.com/st_stonline_filters.php?exchange=N">ST Filter report</a>. </div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-89602939492136940072014-04-11T06:50:00.001-04:002014-04-11T06:50:47.044-04:00Stock Trends Inference Model Select stocks<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
The new Stock Trends Inference Model (ST-IM) Select stocks report has been published for a few weeks now, and reports for previous weeks are also being populated gradually. Subscribers can monitor the current selections to see how they perform. The inference model is an application that translates the Stock Trends data into a unique actionable tool for investors. Let’s review the methodology again.</div>
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The ST-IM Select stocks report includes all stocks with a Stock Trends indicator combination that show statistical evidence of predicting future performance better than base period random returns (see <a href="http://www.stocktrends.com/show_article.php?id=78">The random outcome benchmark</a>). For instance, last week’s ST-IM report for the New York Stock Exchange includes the SPDR Retail exchange traded fund (<a href="http://www.stocktrends.com/streport.php?symbol=XRT-N&s=3&p=6">XRT</a>). The current Stock Trends Report for XRT shows that the ETF has been in a Bullish category for 120 weeks and has sported a strong Bullish indicator for the past 7 weeks. It is under-performing the S&P 500 by 5% over the past 13-weeks (RSI 95), but out-performed the benchmark market index last week (RSI +/- shows a +). There is no unusual volume indicator.</div>
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<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140410_XRT.gif" height="384" width="640" /></div>
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This Stock Trends indicator combination is matched by 63 similar combinations in the 30+ year Stock Trends data history. When these groupings are applied stocks with a share price lower than $2 are not included with stocks with a share price $2 and higher. Also, indicator combinations with weekly volume of trading below 100,000 are grouped separately. The resultant sample that fits the current Stock Trends indicator combination of XRT is shown below:</div>
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<table cellpadding="0" cellspacing="0" class="GCG2UJHDHAB ace_text-layer ace_line GCG2UJHDBT" style="background-color: #e1e2e5; border: none; color: black; cursor: text; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; outline: none; padding-bottom: 8px; padding-left: 6px; white-space: pre-wrap !important; width: 650px; word-wrap: break-word;"><tbody>
<tr><td align="left" style="font-size: 10.4pt !important; line-height: 1.1; vertical-align: top;"><pre class="GCG2UJHDNAB" style="-webkit-user-select: text; border: none; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; line-height: 1.1; outline: none; white-space: pre-wrap !important; word-break: break-all;" tabindex="0"> weekdate exchange symbol X4wk X13wk X40wk
1 1983-05-20 N CEG -6.41 -2.43 -1.55
2 1986-07-11 N DF -4.26 -13.03 -8.78
3 1986-11-07 N CNL 0.69 1.38 -1.50
4 1987-09-11 N TIN 0.00 -29.41 -18.49
5 1992-12-25 N PPL -1.36 7.60 7.17
6 1993-04-16 N CCK -6.72 -11.54 -2.23
7 1993-05-21 N DSM 1.16 3.56 -6.07
8 1994-01-21 N SO -5.50 -8.09 -7.54
9 1995-05-26 N SWY 6.26 5.93 58.72
10 1995-07-14 N HMA 12.67 9.93 73.97
11 1995-07-21 Q ABCW 6.18 28.96 27.30
12 1996-05-03 N IVC -0.95 16.19 -1.90
13 1996-06-28 N BDX -6.68 9.67 13.41
14 1997-04-04 N RDN 12.51 43.51 67.53
15 1997-05-30 N WBS 9.94 31.35 58.02
16 1997-05-30 T NDN 9.54 3.49 22.02
17 1997-05-30 N NWL 3.42 3.27 28.76
18 1997-06-06 N BAC 7.49 -10.09 9.36
19 1997-06-06 N MTB 2.69 10.45 43.28
20 1997-06-13 N BBT 6.26 20.98 49.68
21 1997-06-27 N PDE 9.53 51.13 12.37
22 1997-07-04 N BA 6.00 -5.78 0.00
23 1997-07-18 N CLI 11.30 17.01 9.82
24 1997-07-25 N ESS -3.44 4.17 2.07
25 1997-08-15 N UVV -1.39 4.52 -1.59
26 1997-10-17 N BCE -2.41 9.22 36.60
27 1998-02-13 N TJX 16.49 25.91 31.26
28 1999-07-30 N GD -7.34 -17.65 -15.33
29 2002-12-06 N BKT 0.13 3.17 -8.24
30 2003-05-23 Q PVTB 4.91 40.30 94.47
31 2004-12-24 Q PMTI 9.68 17.77 12.33
32 2005-03-25 Q CHRW -8.19 8.19 42.42
33 2005-06-17 N ATR 0.84 -2.41 9.36
34 2005-07-08 T BXE 20.99 28.48 15.99
35 2005-07-08 N MEE 12.93 17.83 -11.68
36 2005-07-22 Q ESLR -9.35 25.80 109.35
37 2005-08-26 T CNR -1.63 13.66 21.82
38 2005-12-02 Q LUFK 7.04 15.45 26.05
39 2006-01-06 N CVD 10.73 13.87 30.61
40 2006-01-06 N FTO 11.58 43.94 42.36
41 2006-01-27 N GD 2.87 10.75 19.04
42 2006-02-24 T FDG.UN -4.29 -17.25 -48.02
43 2006-09-29 N KSU 8.68 6.11 42.11
44 2006-11-24 Q GOLD -1.01 6.61 5.46
45 2007-02-23 N EME -0.75 3.15 -13.42
46 2007-05-04 N AFG 0.56 -21.43 -23.83
47 2011-03-11 N TSI -0.18 -1.85 -4.07
48 2011-06-03 Q AAPL -0.05 8.91 58.74
49 2011-11-18 N KED 3.96 20.22 26.69
50 2012-01-13 N PRGO -3.82 7.34 21.39
51 2012-02-03 N WCN -4.10 -4.40 -4.49
52 2012-02-03 T CU 9.45 14.68 5.26
53 2012-02-17 N MJN 5.63 7.27 -10.74
54 2012-02-24 T THI 1.65 4.11 -12.52
55 2014-01-03 N STC -1.07 1.83 NA
56 2014-01-24 Q CHTR -4.82 NA NA
57 2014-02-07 T RCH 8.38 NA NA
58 2014-03-21 N AIG NA NA NA
59 2014-03-21 N MMM NA NA NA
60 2014-03-28 N DDM NA NA NA
61 2014-03-28 N UDOW NA NA NA
62 2014-03-28 T MSI NA NA NA
63 2014-04-04 N XRT NA NA NA
64 2014-04-04 Q FELE NA NA NA</pre>
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The table shows the week of the matching combination with subsequent (post-observation) returns for 4-week, 13-week, and 40-week periods. Some of the records at the bottom of the table are too recent to have generated returns for the subsequent periods and are denoted with a “NA”.</div>
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The sample reveals that although there are similar records throughout the data history, they cluster around certain market environments or moments in time. These clusters are important aspects of the samples that we can evaluate in another model, but for the purposes of this inference model they are not significant. We are looking to define a population – all stocks that have a similar quality of trend and price momentum as defined by the Stock Trends indicator combination. From the sample above we can estimate the relevant parameters of this population.</div>
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The sample subsequent returns (4-week, 13-week, 40-week) are the statistics we measure. Here is the summary for the three periods:</div>
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<em>For 4-week CLOSE<strong>*</strong> returns distribution estimation, with 95 % confidence, the 4wk CLOSE mean return of the population of stocks with a similar Stock Trends indicator combination to XRT will be inside [ 1.206 %, 4.282 %]</em></div>
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<em>[1] "With probability of 2.5 % we will have a mean return below 1.206"</em></div>
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<em>Mean return 2.74% and standard deviation of 6.94</em></div>
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<em>Normal Distribution<br />For 4wk CLOSE P(R> 0)=65.37% probability that the 4-week return will be above the base 4-week return (0%).<br /><br />57.89% of 57 sample returns are >0%</em></div>
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<em><br /><br />For 13-week CLOSE returns distribution estimation, with 95 % confidence, the 13wk CLOSE mean return of the population of stocks with a similar Stock Trends indicator combination to XRT will be inside [ 5.044 %, 12.495 %]</em></div>
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<em>[1] "With probability of 2.5 % we will have a mean return below 5.044"</em></div>
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<em>Mean return 8.77% and standard deviation of 16.51</em></div>
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<em>Normal Distribution<br />For 13wk CLOSE P(R> 2.19)=65.49% probability that the 13-week return will be above the base 13-week return (2.19%).<br /><br />72.73% of 55 sample returns are >2.19%</em></div>
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<em><br /><br />For 40-week CLOSE returns distribution estimation, with 95 % confidence, the 40wk CLOSE mean return of the population of stocks with a similar Stock Trends indicator combination to XRT will be inside [ 10.272 %, 24.276 %]</em></div>
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<em>[1] "With probability of 2.5 % we will have a mean return below 10.272"</em></div>
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<em>Mean return 17.27% and standard deviation of 30.73</em></div>
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<em>Normal Distribution<br />For 40wk CLOSE P(R> 6.45)=63.76% prbability that the 40-week return will be above the base 40-week return (6.45%).<br /><br />57.41% of 54 sample returns are >6.45%</em></div>
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<strong>*</strong> Note: The Stock Trends Inference Model uses end-of-period closing price returns. See <a href="http://www.stocktrends.com/show_article.php?id=73">Variability of returns</a></div>
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What does this tells us? First, it is understood that generally we cannot precisely know the true population. We can only estimate it’s characteristics from a given sample. Equipped with two sample statistics – the sample mean (average) return and sample standard deviation (a standardized measure of variance of the returns) – we can estimate the population mean return and standard deviation (both known in statistical parlance as parameters). This magical property you can investigate further in many statistical books that introduce concepts of statistical inference.</div>
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In this example we can see that the lowest value of the interval estimate of the population mean is above the mean return of random returns in each of the three periods. This implies that we are pretty certain that the mean return of this population is higher than the random return benchmarks. If we assume a normal distribution of returns for the population – which we do because our assumption is that returns are random – then we can use another statistical method to give the probabilities that XRT will return above the random mean return.</div>
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<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140410_XRT13.png" height="430" width="640" /></div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<em style="font-size: small;">The sample density distribution is filled in green. The assumed population distribution - a normal distribution - is outlined in blue. The vertical yellow line indicates the estimated population mean return. The vertical red line indicates the base return of a randomly selected stock. </em></div>
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In the case of the 13-week period ahead, the Stock Trends inference model posits that there is a 65.5% chance that XRT will return above 2.19%. That is better than the 50% chance a random return will generate a 13-week return better than 2.19%, but we should always remember that unless a probability is 1 (100%) there is no certainty. You can always roll a negative outcome even if the probability of a positive outcome is 99%. However, a 65.5% chance is an edge a trader can use.</div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<br />
The ST-IM report gives us a weekly round-up of stocks that have at least a 55% probability of generating a 13-week return better than 2.19%. There are other indicator combinations that also share this property, but these are the ones that meet the criteria of having a confidence interval above the base mean return of each period (others may have lower estimates in the interval that fall below the base mean return). These are the ones that we are most certain will have a population mean return above the base return of every period.</div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<br />
Another important aspect of the Stock Trends Inference Model demands more attention. Each ST-IM report represents a sample of a new population, namely all stocks that fit the model criteria. The Central Limit Theorem states that the mean return of random samples from this population will be normally distributed (bell-shaped). We can also estimate that a portfolio of stocks randomly selected from the ST-IM reports will return above the base market return.</div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<br />
Let’s experiment. We can construct many randomly selected sample portfolios from the ST-IM Select stocks reports, with equal amounts invested in each stock or ETF. What kind of 13-week returns were generated?</div>
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<br />
There have been 5,905 ST-IM Select stocks in the past year that generated subsequent 13-week returns (ST-IM Select reports from April 4, 2013 to January 3, 2014). This sample can be summarized as follows:</div>
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<br /></div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140410_ST-IMreturns.png" height="430" width="640" /></div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<em style="font-size: small;">The sample density distribution is filled in green. The vertical yellow line indicates the sample mean return. The vertical red line indicates the base return of a randomly selected stock. </em></div>
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<br /></div>
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<br /></div>
<table cellpadding="0" cellspacing="0" class="GCG2UJHDHAB ace_text-layer ace_line GCG2UJHDBT" style="background-color: #e1e2e5; border: none; color: black; cursor: text; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; outline: none; padding-bottom: 8px; padding-left: 6px; white-space: pre-wrap !important; width: 650px; word-wrap: break-word;"><tbody>
<tr><td align="left" style="font-size: 10.4pt !important; line-height: 1.1; vertical-align: top;"><pre class="GCG2UJHDNAB" style="-webkit-user-select: text; border: none; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; line-height: 1.1; outline: none; white-space: pre-wrap !important; word-break: break-all;" tabindex="0"> vars n mean sd median trimmed mad min max range skew kurtosis se
1 1 5905 8.75 19.89 6.4 7.11 12.6 -59.5 336.8 396.3 2.67 22.65 0.26
</pre>
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The mean (average)13-week return of these ST-IM Select stocks is 8.8%. The maximum 13-week return was 396%, the biggest loss 60%. Our inference model directs us toward stocks that have a higher probability of returns greater than the mean 13-week return of randomly selected stocks – 2.19%. The results confirm that – 64% of ST-IM Select stocks had a return greater than 2.19%. But how did these ST-IM Select stocks do in comparison to the benchmark market indexes? The following gives a summary of the Stock Trends RSI values of the select stocks 13-weeks after the selections:</div>
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<br /></div>
<table cellpadding="0" cellspacing="0" class="GCG2UJHDHAB ace_text-layer ace_line GCG2UJHDBT" style="background-color: #e1e2e5; border: none; color: black; cursor: text; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; outline: none; padding-bottom: 8px; padding-left: 6px; white-space: pre-wrap !important; width: 650px; word-wrap: break-word;"><tbody>
<tr><td align="left" style="font-size: 10.4pt !important; line-height: 1.1; vertical-align: top;"><pre class="GCG2UJHDNAB" style="-webkit-user-select: text; border: none; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; line-height: 1.1; outline: none; white-space: pre-wrap !important; word-break: break-all;" tabindex="0"> vars n mean sd median trimmed mad min max range skew kurtosis se
1 1 5905 104.06 18.91 101 102.49 11.86 37 417 380 2.72 23.44 0.25
</pre>
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<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
The mean Stock Trends RSI is 104. This tells us that had we invested in all of these ST-IM Select stocks our performance would have exceeded the market outcomes – we would have done better than trading simultaneously in a benchmark exchange traded fund like the SPDR S&P 500 ETF (SPY).</div>
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<br />
Obviously, it is not practical to look at the total of these numerous selections. We would have had to trade a much smaller number of ST-IM Select stocks. It’s difficult to isolate which subset of ST-IM Select stocks would have generated the best returns in this distribution (although I will try to do this in the future using data mining analysis techniques), but we can estimate the average or likely return attainable by random sampling.</div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<br />
How would have investor done if he randomly selected small portfolios of stocks from the ST-IM reports? For example, what results would have been attainable if we randomly selected five (5) stocks from the ST-IM reports and measured subsequent 13-week returns of these portfolios? Does the ST-IM model deliver superior returns for a retail trader?</div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<br />
If we take 1,000 random portfolios of 5 stocks from our sample, the following distributions of portfolio returns and RSI values is evident after the 13-week period for each portfolio:</div>
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<br /></div>
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<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140410_samplereturns.png" height="640" width="633" /></div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<em style="font-size: small;">The sample portfolio returns density distribution is filled in green. The vertical yellow line indicates the portfolio mean return. The vertical red line indicates the base return of a randomly selected stock. </em></div>
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<br /></div>
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<br /></div>
<table cellpadding="0" cellspacing="0" class="GCG2UJHDHAB ace_text-layer ace_line GCG2UJHDBT" style="background-color: #e1e2e5; border: none; color: black; cursor: text; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; outline: none; padding-bottom: 8px; padding-left: 6px; white-space: pre-wrap !important; width: 650px; word-wrap: break-word;"><tbody>
<tr><td align="left" style="font-size: 10.4pt !important; line-height: 1.1; vertical-align: top;"><pre class="GCG2UJHDNAB" style="-webkit-user-select: text; border: none; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; line-height: 1.1; outline: none; white-space: pre-wrap !important; word-break: break-all;" tabindex="0"> vars n mean sd median trimmed mad min max range skew kurtosis se
1 1 1000 8.53 7.72 7.72 8.11 7.03 -13.64 46.08 59.72 0.71 1.48 0.24
</pre>
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The mean 13-week return of these portfolios is 8.5%. That translates to an annualized return of 34%. Of these 1,000 random portfolios, 79% generated a 13-week return greater than the base period return of 2.19%.</div>
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<br />
Below is a summary of how these portfolios did relative to the benchmark indexes over these 13-week periods.</div>
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<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140410_samplersi.png" height="430" width="640" /></div>
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<em style="font-size: small;">The sample portfolio post-trade 13-week RSI density distribution is filled in green. The vertical yellow line indicates the portfolio mean post-trade RSI. The vertical red line indicates the base benchmark index.</em></div>
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<table cellpadding="0" cellspacing="0" class="GCG2UJHDHAB ace_text-layer ace_line GCG2UJHDBT" style="background-color: #e1e2e5; border: none; color: black; cursor: text; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; outline: none; padding-bottom: 8px; padding-left: 6px; white-space: pre-wrap !important; width: 650px; word-wrap: break-word;"><tbody>
<tr><td align="left" style="font-size: 10.4pt !important; line-height: 1.1; vertical-align: top;"><pre class="GCG2UJHDNAB" style="-webkit-user-select: text; border: none; font-family: Consolas, 'Lucida Console', monospace; font-size: 10.4pt !important; line-height: 1.1; outline: none; white-space: pre-wrap !important; word-break: break-all;" tabindex="0"> vars n mean sd median trimmed mad min max range skew kurtosis se
1 1 1000 103.83 7.49 103.2 103.44 6.82 82.4 140.2 57.8 0.66 1.31 0.24
</pre>
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The mean 13-week RSI is 104. This tells us that the random portfolios are outperforming the market, on average, by about 4% in these13-week trades. Take note that although transaction costs are not discounted here, we are comparing against an active trading of a market index, not a buy-and-hold strategy. A buy-and-hold strategy can be compared against the ST-IM portfolio annualized mean return of 34%. The S&P 500 index is up 20% in the past 12-months; the S&P/TSX Composite Index is up 17%. In this comparison the ST-IM annualized return should be discounted for transaction costs.</div>
<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
<br />
Subscribers to <a href="http://www.stocktrends.com/member_subscriptions.php">Stock Trends Weekly Reporter</a> should feel quite confident in actively trading the highlighted stocks in the weekly ST-IM Select stocks report.<br />
<br />
Learn more about the Stock Trends Inference Model at <a href="http://www.stocktrends.com/">www.stocktrends.com</a></div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-657243577540541742014-03-09T11:16:00.000-04:002014-04-11T06:52:38.696-04:00Select stocks<table style="width: 75%px;"><tbody>
<tr><td>While Stock Trends reports are designed to highlight stocks that have triggered some aspect of traditional charting, the new inference model introduced in recent editorials is fundamentally a more data driven approach to the Stock Trends indicators. It is an apparatus that looks at statistically measured responses to a market condition defined by price trend and momentum. If a stock has certain market conditions, is there a statistical probability of future returns?<br />
<div>
Now that we’ve been introduced to the elements of the model (see <a href="http://www.stocktrends.com/stArticle.php">recent editorials</a>), let’s look at what the current Stock Trends indicators say. Below are a series of heatmap images that show various rankings of the probable returns in the upcoming 13-week period. Those at the top of the heatmap have the highest probability of exceeding the base period random returns.</div>
<div>
</div>
<div>
The colour coding indicates the relative returns above the base period returns (4-week: 0%, 13-week: 2.19%, 40-week: 6.45%). As the statistical mean of the returns generated by the Stock Trends indicator combinations out-perform the base returns the green colour is progressively darker. As the statistical mean of the returns generated by the Stock Trends indicator combinations under-perform the base returns the red colour is progressively darker. Returns centered around the period base returns are colour-coded yellow. The included Color Key illustrates the coding.</div>
<div>
</div>
<div>
First we will look at the top 30 ranked ‘Select’ North American stocks. Select stocks must have Stock Trends indicator combinations that have a lower limit of its mean return confidence interval above the base period mean return for all three periods – 4-week, 13-week, and 40-week. These select stocks are then ranked by descending order of their probability of outperforming the base period mean random return. The assumed probability distribution is a normal curve.</div>
<div>
</div>
<div>
In short, these stocks exhibit the Stock Trends indicator combinations with the highest probability of beating the mean return of randomly selected stocks. Future editorials will elaborate on the portfolio management implications of this ranking system. For now, let’s look at some analysis results:<br />
<br /></div>
<div>
</div>
<div>
</div>
</td></tr>
<tr bgcolor="#c5d4b3" valign="middle"><td><h2 style="text-align: center;">
Stock Trends inference model</h2>
</td></tr>
<tr bgcolor="#ebf6e9" valign="middle"><td><h2 style="text-align: center;">
Select Stocks - Top 30</h2>
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<tr><td><h2 style="text-align: center;">
</h2>
<div style="text-align: center;">
<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140309/20140307_heatmap_top.png" /></div>
<div style="text-align: center;">
<strong>Current Trend Listings of Select Top 30 stocks</strong></div>
<div style="text-align: center;">
</div>
<div>
<table align="center"><tbody>
<tr bgcolor="#009900"><td><h4>
Trend</h4>
</td><td><h4>
Issue</h4>
</td><td><h4 align="center">
Price($)</h4>
</td><td><h4 align="center">
% chg</h4>
</td><td><h4 align="center">
Vol(00s)</h4>
</td><td><h4>
</h4>
</td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=HA-Q&s=3&p=6">Hawaiian Holdings (HA)</a></td><td><div align="right">
14.20</div>
</td><td><div align="right">
17.9</div>
</td><td><div align="right">
154139</div>
</td><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidStar2.gif" /></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=GORO-A&s=3&p=6">Gold Resource (GORO)</a></td><td><div align="right">
5.52</div>
</td><td><div align="right">
7.2</div>
</td><td><div align="right">
23076</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=DEPO-Q&s=3&p=6">Depomed Inc. (DEPO)</a></td><td><div align="right">
13.70</div>
</td><td><div align="right">
13.7</div>
</td><td><div align="right">
159944</div>
</td><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidStar2.gif" /></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=GOL-N&s=3&p=6">Gol Linhas Aereas Intelig. SA (GOL)</a></td><td><div align="right">
4.42</div>
</td><td><div align="right">
-9.2</div>
</td><td><div align="right">
48506</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=FICO-N&s=3&p=6">Fair Isaac Corporation (FICO)</a></td><td><div align="right">
53.65</div>
</td><td><div align="right">
-0.2</div>
</td><td><div align="right">
10056</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=EMKR-Q&s=3&p=6">Emcore Corp. (EMKR)</a></td><td><div align="right">
5.16</div>
</td><td><div align="right">
5.7</div>
</td><td><div align="right">
10057</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=BBH-N&s=3&p=6">Mkt Vectors Biotech ETF (BBH)</a></td><td><div align="right">
99.88</div>
</td><td><div align="right">
-2.4</div>
</td><td><div align="right">
10260</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=BXE-T&s=3&p=6">Bellatrix Exploration (BXE)</a></td><td><div align="right">
8.95</div>
</td><td><div align="right">
5.9</div>
</td><td><div align="right">
69745</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ZIXI-Q&s=3&p=6">Zix Corporation (D) (ZIXI)</a></td><td><div align="right">
4.61</div>
</td><td><div align="right">
2.0</div>
</td><td><div align="right">
13777</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=QTWW-Q&s=3&p=6">Quantum Fuel Systems Tech. (QTWW)</a></td><td><div align="right">
10.25</div>
</td><td><div align="right">
18.8</div>
</td><td><div align="right">
87660</div>
</td><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidStar2.gif" /></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CMI-N&s=3&p=6">Cummins (CMI)</a></td><td><div align="right">
145.62</div>
</td><td><div align="right">
-0.2</div>
</td><td><div align="right">
62808</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=SDLP-N&s=3&p=6">Seadrill Partners LLC (SDLP)</a></td><td><div align="right">
31.78</div>
</td><td><div align="right">
1.2</div>
</td><td><div align="right">
5573</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=QQQX-Q&s=3&p=6">NASDAQ Pr Income and Growth (QQQX)</a></td><td><div align="right">
18.50</div>
</td><td><div align="right">
1.1</div>
</td><td><div align="right">
2387</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=MEG-N&s=3&p=6">Media General (MEG)</a></td><td><div align="right">
17.91</div>
</td><td><div align="right">
-5.6</div>
</td><td><div align="right">
10951</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=SVA-Q&s=3&p=6">Sinovac Biotech (SVA)</a></td><td><div align="right">
6.52</div>
</td><td><div align="right">
-0.8</div>
</td><td><div align="right">
23489</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=JEC-N&s=3&p=6">Jacobs Engineering Group (JEC)</a></td><td><div align="right">
63.09</div>
</td><td><div align="right">
4.0</div>
</td><td><div align="right">
49521</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ENDP-Q&s=3&p=6">Endo International plc (ENDP)</a></td><td><div align="right">
73.76</div>
</td><td><div align="right">
-7.6</div>
</td><td><div align="right">
220337</div>
</td><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidStar2.gif" /></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CHTR-Q&s=3&p=6">Charter Communications (CHTR)</a></td><td><div align="right">
127.00</div>
</td><td><div align="right">
0.2</div>
</td><td><div align="right">
87261</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=XLNX-Q&s=3&p=6">Xilinx Inc. (XLNX)</a></td><td><div align="right">
53.03</div>
</td><td><div align="right">
1.6</div>
</td><td><div align="right">
186707</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=AHS-N&s=3&p=6">AMN Healthcare Services Inc. (AHS)</a></td><td><div align="right">
14.47</div>
</td><td><div align="right">
3.9</div>
</td><td><div align="right">
16981</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=APD-N&s=3&p=6">Air Products & Chemicals (APD)</a></td><td><div align="right">
121.67</div>
</td><td><div align="right">
0.3</div>
</td><td><div align="right">
47689</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=XEC-N&s=3&p=6">Cimarex Energy (XEC)</a></td><td><div align="right">
114.21</div>
</td><td><div align="right">
-1.3</div>
</td><td><div align="right">
60310</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CUI-Q&s=3&p=6">CUI Global (CUI)</a></td><td><div align="right">
8.73</div>
</td><td><div align="right">
0.0</div>
</td><td><div align="right">
4092</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=HEI-N&s=3&p=6">Heico Corp. (HEI)</a></td><td><div align="right">
63.35</div>
</td><td><div align="right">
1.9</div>
</td><td><div align="right">
12675</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CTRP-Q&s=3&p=6">Ctrip.com International (CTRP)</a></td><td><div align="right">
52.59</div>
</td><td><div align="right">
-2.6</div>
</td><td><div align="right">
146452</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=RUSHA-Q&s=3&p=6">Rush Enterprises (RUSHA)</a></td><td><div align="right">
29.30</div>
</td><td><div align="right">
2.5</div>
</td><td><div align="right">
5680</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=TRW-N&s=3&p=6">TRW Automotive Holdings (TRW)</a></td><td><div align="right">
82.62</div>
</td><td><div align="right">
0.4</div>
</td><td><div align="right">
44648</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ANH-N&s=3&p=6">Anworth Mortgage Asset Co (ANH)</a></td><td><div align="right">
5.09</div>
</td><td><div align="right">
-1.7</div>
</td><td><div align="right">
77612</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=IGM-T&s=3&p=6">IGM Financial (IGM)</a></td><td><div align="right">
54.65</div>
</td><td><div align="right">
1.2</div>
</td><td><div align="right">
9237</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=MCK-N&s=3&p=6">McKesson Corp. (MCK)</a></td><td><div align="right">
182.40</div>
</td><td><div align="right">
3.0</div>
</td><td><div align="right">
67988</div>
</td><td></td></tr>
</tbody></table>
</div>
<div>
</div>
<div>
Subscribers to <strong>Stock Trends Weekly Reporter</strong> will find a new report listed under the header 'ST-IM Select stocks of the week' in the <a href="http://www.stocktrends.com/st_stonline_filters.php?exchange=N">ST Filter Listings section</a>.</div>
<div>
</div>
<div>
Below are the distributions of returns generated by the Stock Trends indicator combination now sported by Hawaiian Holdings (HA-Q), the top ranked Select stock.</div>
<div style="text-align: center;">
<img align="center" alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140309/20140309_HA_4wk.png" height="311" width="400" /></div>
<div>
<span style="font-size: x-small;"><em>Note: the sample density distribution is outlined in green. The assumed population distribution - a normal distribution - is outlined in blue. The vertical yellow line indicates the estimated population mean return. The vertical red line indicates the base return of a randomly selected stock. </em></span></div>
<div>
</div>
<div>
</div>
<div style="margin-left: 40px;">
For 4-week CLOSE returns distribution estimation, with 95 % confidence, the 4-week CLOSE mean return of the population of stocks</div>
<div style="margin-left: 40px;">
with a similar Stock Trends indicator combination to HA will be inside [ 1.978 %, 11.294 %]</div>
<div style="margin-left: 40px;">
[1] "With probability of 2.5% we will have a mean return below 1.978%"</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
Estimated population mean return 6.64% and standard deviation of 19.85</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
Normal Distribution</div>
<div style="margin-left: 40px;">
<strong> For 4wk CLOSE P(R> 0)=63.09% probability of a return greater than the 4-week mean return of a randomly selected stock.</strong></div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
62.75% of sample returns are >0%</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px; text-align: center;">
<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140309/20140309_HA_13wk.png" height="311" width="400" /></div>
<div style="margin-left: 40px;">
<em style="font-size: small;">Note: the sample density distribution is outlined in green. The assumed population distribution - a normal distribution - is outlined in blue. The vertical yellow line indicates the estimated population mean return. The vertical red line indicates the base return of a randomly selected stock. </em></div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
For 13-week CLOSE returns distribution estimation, with 95 % confidence, the 13-week CLOSE mean return of the population of stocks</div>
<div style="margin-left: 40px;">
with a similar Stock Trends indicator combination to HA will be inside [ 10.276 %, 33.502 %]</div>
<div style="margin-left: 40px;">
[1] "With probability of 2.5 % we will have a mean return below 10.276%"</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
Estimated population mean return 21.89% and standard deviation of 46.9</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
Normal Distribution</div>
<div style="margin-left: 40px;">
<strong> For 13wk CLOSE P(R> 2.19)=66.28% probability of a return greater than the 13-week mean return of a randomly selected stock.</strong></div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
63.04% of sample returns are >2.19%</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px; text-align: center;">
<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140309/20140309_HA_40wk.png" height="311" width="400" /></div>
<div style="margin-left: 40px;">
<em style="font-size: small;">Note: the sample density distribution is outlined in green. The assumed population distribution - a normal distribution - is outlined in blue. The vertical yellow line indicates the estimated population mean return. The vertical red line indicates the base return of a randomly selected stock. </em></div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
For 40-week CLOSE returns distribution estimation, with 95 % confidence, the 40-week CLOSE mean return of the population of stocks</div>
<div style="margin-left: 40px;">
with a similar Stock Trends indicator combination to HA will be inside [ 9.773 %, 37.583 %]</div>
<div style="margin-left: 40px;">
[1] "With probability of 2.5 % we will have a mean return below 9.773%"</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
Estimated mean return 23.68% and standard deviation of 54.21</div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
Normal Distribution</div>
<div style="margin-left: 40px;">
<strong> For 40wk CLOSE P(R> 6.45)=62.47% probability of a return greater than the 40-week mean return of a randomly selected stock.</strong></div>
<div style="margin-left: 40px;">
</div>
<div style="margin-left: 40px;">
55.81% of sample returns are >6.45%</div>
<div>
</div>
<div>
</div>
<div>
Next we'll survey the mean return expectations of the current Picks of the Week, S&P 100 stocks, and S&P/TSX stocks. Here the stocks are ranked by their mean average returns across all three periods (4-week, 13-week, and 40-week). </div>
<div>
</div>
<div>
</div>
</td></tr>
<tr bgcolor="#ebf6e9" valign="middle"><td><h2 style="text-align: center;">
Picks of the Week</h2>
</td></tr>
<tr><td><h2 style="text-align: center;">
</h2>
<h3 style="text-align: center;">
Current NYSE Picks</h3>
</td></tr>
<tr><td><div>
</div>
<div style="text-align: center;">
<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140309/20140307_heatmap_picks_N.png" /></div>
<div>
It should be noted that not all current Picks of the Week, or index components listed below have statistical inference ratings. Some Stock Trends indicator combinations do not provide a large enough sample size to make inferences about their population. The heatmap images displayed here are great for viewing relative performance expectations, but not the best to provide links to the Stock Trends Reports on the individual stocks. Below is a ranked table that provides the current trend listing of these Picks of the Week, with links to their Stock Trends Report pages.</div>
<div>
</div>
<div style="text-align: center;">
<strong>Current Trend Listings of NYSE Picks of the week stocks</strong></div>
<div>
</div>
<div>
<table align="center"><tbody>
<tr bgcolor="#009900"><td><h4>
Trend</h4>
</td><td><h4>
Issue</h4>
</td><td><h4 align="center">
Price($)</h4>
</td><td><h4 align="center">
% chg</h4>
</td><td><h4 align="center">
Vol(00s)</h4>
</td><td><h4>
</h4>
</td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidSquare2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=AU-N&s=3&p=6">Anglogold Ashanti Ltd. (AU)</a></td><td><div align="right">
18.62</div>
</td><td><div align="right">
5.9</div>
</td><td><div align="right">
160670</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=GLDX-N&s=3&p=6">Global X Gold Explorers E.T.F. (GLDX)</a></td><td><div align="right">
16.19</div>
</td><td><div align="right">
5.7</div>
</td><td><div align="right">
5652</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=PQ-N&s=3&p=6">PetroQuest Energy (PQ)</a></td><td><div align="right">
5.17</div>
</td><td><div align="right">
9.3</div>
</td><td><div align="right">
53759</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=DAN-N&s=3&p=6">Dana Holding (DAN)</a></td><td><div align="right">
22.15</div>
</td><td><div align="right">
2.2</div>
</td><td><div align="right">
83552</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=SSI-N&s=3&p=6">Stage Stores (SSI)</a></td><td><div align="right">
24.36</div>
</td><td><div align="right">
23.0</div>
</td><td><div align="right">
54502</div>
</td><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidStar2.gif" /></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=WFT-N&s=3&p=6">Weatherford (WFT)</a></td><td><div align="right">
17.07</div>
</td><td><div align="right">
2.4</div>
</td><td><div align="right">
539518</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidSquare2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=EMC-N&s=3&p=6">EMC Corp. (EMC)</a></td><td><div align="right">
27.04</div>
</td><td><div align="right">
2.5</div>
</td><td><div align="right">
970645</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=COO-N&s=3&p=6">Cooper Cos. (COO)</a></td><td><div align="right">
136.48</div>
</td><td><div align="right">
6.5</div>
</td><td><div align="right">
39638</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CRI-N&s=3&p=6">Carters Inc. (CRI)</a></td><td><div align="right">
77.48</div>
</td><td><div align="right">
2.9</div>
</td><td><div align="right">
33910</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=AIT-N&s=3&p=6">Appld Industrial Technologies (AIT)</a></td><td><div align="right">
51.66</div>
</td><td><div align="right">
1.2</div>
</td><td><div align="right">
7604</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidSquare2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=WES-N&s=3&p=6">Western Gas Partners LP (WES)</a></td><td><div align="right">
64.39</div>
</td><td><div align="right">
1.7</div>
</td><td><div align="right">
10911</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=SCI-N&s=3&p=6">Service Corp. International (SCI)</a></td><td><div align="right">
19.63</div>
</td><td><div align="right">
5.0</div>
</td><td><div align="right">
111258</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=BRK.B-N&s=3&p=6">Berkshire Hathaway (BRK.B)</a></td><td><div align="right">
122.67</div>
</td><td><div align="right">
6.0</div>
</td><td><div align="right">
225745</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=SSS-N&s=3&p=6">Sovran Self Storage (SSS)</a></td><td><div align="right">
75.00</div>
</td><td><div align="right">
1.4</div>
</td><td><div align="right">
15939</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=TSM-N&s=3&p=6">Taiwan Semiconductor (TSM)</a></td><td><div align="right">
18.77</div>
</td><td><div align="right">
3.9</div>
</td><td><div align="right">
689911</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=RGP-N&s=3&p=6">Regency Energy Partners LP (RGP)</a></td><td><div align="right">
27.38</div>
</td><td><div align="right">
4.3</div>
</td><td><div align="right">
21744</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidSquare2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ENB-N&s=3&p=6">Enbridge Inc. (ENB)</a></td><td><div align="right">
43.83</div>
</td><td><div align="right">
3.6</div>
</td><td><div align="right">
44262</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CVC-N&s=3&p=6">Cablevision Systems (CVC)</a></td><td><div align="right">
18.15</div>
</td><td><div align="right">
3.1</div>
</td><td><div align="right">
137589</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=RLD-N&s=3&p=6">RealD Inc. (RLD)</a></td><td><div align="right">
11.50</div>
</td><td><div align="right">
4.1</div>
</td><td><div align="right">
15497</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=RJA-N&s=3&p=6">ELEMENTS Intl Commodity Agric. (RJA)</a></td><td><div align="right">
8.86</div>
</td><td><div align="right">
4.4</div>
</td><td><div align="right">
9428</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=BKS-N&s=3&p=6">Barnes & Noble (BKS)</a></td><td><div align="right">
21.20</div>
</td><td><div align="right">
10.7</div>
</td><td><div align="right">
92892</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=MTRN-N&s=3&p=6">Materion Corp. (MTRN)</a></td><td><div align="right">
33.07</div>
</td><td><div align="right">
11.8</div>
</td><td><div align="right">
8897</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=SPPP-N&s=3&p=6">Sprott Phys. Platinum & Palla. (SPPP)</a></td><td><div align="right">
9.82</div>
</td><td><div align="right">
7.0</div>
</td><td><div align="right">
17007</div>
</td><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidStar2.gif" /></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=HDB-N&s=3&p=6">HDFC Bank (HDB)</a></td><td><div align="right">
37.33</div>
</td><td><div align="right">
11.1</div>
</td><td><div align="right">
75285</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=AIV-N&s=3&p=6">Apartment Invt & Mgmt Co. (AIV)</a></td><td><div align="right">
30.64</div>
</td><td><div align="right">
2.5</div>
</td><td><div align="right">
86576</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=STM-N&s=3&p=6">STMicroelectronics (STM)</a></td><td><div align="right">
9.35</div>
</td><td><div align="right">
3.5</div>
</td><td><div align="right">
66572</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=PTM-N&s=3&p=6">E-TRACS UBS Long Platinum (PTM)</a></td><td><div align="right">
16.66</div>
</td><td><div align="right">
3.1</div>
</td><td><div align="right">
3118</div>
</td><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidStar2.gif" /></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=PNY-N&s=3&p=6">Piedmont Natural Gas (PNY)</a></td><td><div align="right">
34.25</div>
</td><td><div align="right">
1.3</div>
</td><td><div align="right">
17919</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=POT-N&s=3&p=6">Potash Corp. of Saskatchewan (POT)</a></td><td><div align="right">
34.69</div>
</td><td><div align="right">
4.2</div>
</td><td><div align="right">
344924</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=RJI-N&s=3&p=6">ELEMENTS Intl Commodity (RJI)</a></td><td><div align="right">
8.62</div>
</td><td><div align="right">
1.4</div>
</td><td><div align="right">
40932</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=FFC-N&s=3&p=6">Flaherty&Crumrine Pref. Secur. (FFC)</a></td><td><div align="right">
18.75</div>
</td><td><div align="right">
1.4</div>
</td><td><div align="right">
11653</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=HL-N&s=3&p=6">Hecla Mining (HL)</a></td><td><div align="right">
3.46</div>
</td><td><div align="right">
2.4</div>
</td><td><div align="right">
237539</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=RKUS-N&s=3&p=6">Ruckus Wireless, Inc. (RKUS)</a></td><td><div align="right">
15.19</div>
</td><td><div align="right">
8.5</div>
</td><td><div align="right">
103125</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=WBK-N&s=3&p=6">Westpac Banking (WBK)</a></td><td><div align="right">
30.76</div>
</td><td><div align="right">
2.0</div>
</td><td><div align="right">
6111</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=EOD-N&s=3&p=6">Wells Fargo Adv. Glb Div. Opp. (EOD)</a></td><td><div align="right">
7.80</div>
</td><td><div align="right">
1.2</div>
</td><td><div align="right">
13421</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=HTA-N&s=3&p=6">Healthcare Trust of America (HTA)</a></td><td><div align="right">
11.46</div>
</td><td><div align="right">
2.1</div>
</td><td><div align="right">
111653</div>
</td><td></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=GDXJ-N&s=3&p=6">Mrk Vectr Jr Gold Miner E.T.F. (GDXJ)</a></td><td><div align="right">
42.50</div>
</td><td><div align="right">
2.8</div>
</td><td><div align="right">
138210</div>
</td><td></td></tr>
</tbody></table>
<br /></div>
</td></tr>
<tr><td><h3 style="text-align: center;">
Current Nasdaq Picks</h3>
<h3 style="text-align: center;">
</h3>
</td></tr>
<tr><td><div>
</div>
<div style="text-align: center;">
<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140309/20140307_heatmap_picks_Q.png" /></div>
<div>
</div>
</td></tr>
<tr><td><h3 style="text-align: center;">
Current TSX Picks</h3>
<h3 style="text-align: center;">
</h3>
</td></tr>
<tr><td><div>
</div>
<div style="text-align: center;">
<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140309/20140307_heatmap_picks_T.png" /></div>
<div>
</div>
</td></tr>
<tr bgcolor="#ebf6e9" valign="middle"><td><h2 style="text-align: center;">
S & P 100 index stocks</h2>
</td></tr>
<tr><td><div>
</div>
<div style="text-align: center;">
<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140309/20140307_heatmap_oex.png" /></div>
<div>
</div>
</td></tr>
<tr bgcolor="#ebf6e9" valign="middle"><td><h2 style="text-align: center;">
S & P/TSX 60 index stocks</h2>
</td></tr>
<tr><td><div>
</div>
<div style="text-align: center;">
<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140309/20140307_heatmap_sptsx60.png" /></div>
<div>
</div>
</td></tr>
</tbody></table>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-45621844543724460842014-03-07T11:05:00.002-05:002014-03-07T11:05:43.318-05:00Understanding our assumptions<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
The Stock Trends inference model is built around two central premises: (1) market conditions are non-specific to a particular security, and (2) market responses to market conditions are specific. These assumptions are crucial legs on which technical analysis stands. They state that price and volume patterns are homogeneous across markets – that a bull trend in one instrument is comparable to a bull trend in another, for instance – and that future price movements can be inferred or extrapolated from these patterns. Let’s better understand this analysis departure point.</div>
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The first premise might strike you as more of a theoretical foundation than an empirical one. How can we really say that market conditions – however defined – are structurally consistent among all stocks? How can the market conditions, at one point in time, of a highly liquid financial stock like Bank of America (<a href="http://www.stocktrends.com/streport.php?symbol=BAC-N&s=3&p=6">BAC-N</a>), for instance, compare to that of a technology stock like EchoStar Corp. (<a href="http://www.stocktrends.com/streport.php?symbol=SATS-Q&s=3&p=6">SATS-Q</a>) or an industrial stock like Illinois Tool Works (<a href="http://www.stocktrends.com/streport.php?symbol=ITW-N&s=3&p=6">ITW-N</a>) at other points in time?</div>
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Average daily trading volume of BAC is over 100 million shares, while average daily trading volume of ITW is about 2.1-million shares and SATS only trades about 250,000 shares a day. The market cap of BAC is $175-billion. The market cap of ITW and SATS is $35-billion and $4.5-billion, respectively. There are significant differences in the size of the market for these stocks, and even bigger differences if we looked at small cap stocks that could be grouped with BAC based on our definition of equivalent market conditions – that is, similar Stock Trends indicator combinations. And certainly these stocks are different beasts in terms of their industry categories. Are price trends and price momentum in these various markets comparable?</div>
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When we say that market conditions are non-specific to a particular security we mean that the price mechanism balancing demand and supply is analogous, even if the range and scope of factors influencing that balance differs. Technical analysis places priority on price and volume change factors as inputs influencing a market equilibrium. Investors respond to the market through various signals of price, market breadth, and trading intensity. These market conditions are dynamic, constantly shifting the balance of supply and demand to a new equilibrium.</div>
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So, if the shares of Bank of America have been trending positively for two years but the relative price momentum over the most recent three month period is market-neutral – as the current BAC Stock Trends Report infers - we can say something qualitative about the return expectations of investors in BAC. Investors key on past returns - their stability as well as trajectory. As the character of returns change – increased volatility and trading volume, or diminished price momentum, for example – the relative demand for and supply of a security changes. It matters less that the market for the security is the size of BAC or the size of SATS – the same relative responses determine the change in price. Shareowners who want to capture returns or limit losses sell. Investors who see more value buy.</div>
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Because the Stock Trends indicator combinations codify a market condition (by categorizing trend, quantifying the length of trend and relative price momentum) we can apply our assumption about the non-specificity of market conditions and group indicator combinations. It is from these samples of ‘like’ indicator combinations that we can apply statistical inference methods.</div>
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However, we are applying these inference methods on a statistic of the sample – that is, subsequent returns (changes in price). Here we are invoking our second premise – that market responses to market conditions are specific. The qualitative conditions of a market are assumed to be an input variable. The subsequent change in price is said to be a response variable. The measurement of that response is the key component of the Stock Trends inference model. We want to know which market conditions result in the best response – the best probabilities of higher returns.</div>
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This approach is quite different from the typical technical analysis you will find. Much of technical analysis has to do with time series analysis. In its most generic, simplest form you will recognise this as classical charting techniques. Indeed, trend and price support lines are tools used to extrapolate price trajectory. This is the form of technical analysis that presents opinions about price objectives. It is the technical analysis that prognosticates about where the market is headed.</div>
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The Stock Trends inference model departs from this kind of conjecture. Instead, it is a quantitative apparatus that suggests responses to market conditions are random, but centered around categorized populations. The model employs the trend categories and market metrics (RSI, volume indicators, etc.) as inputs, but its output is a probability statement. Elements of charting go in…but what comes out is a probability distribution. A stock is ranked highly not because its chart characteristics suggest it will rally. It is ranked highly because its probabilities of beating the market are favourable.</div>
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Previous editorials have emphasized the randomness of results. Also highlighted is the generalized assumption of a normal distribution of returns, which is the inherent distribution of random data. Evidence of randomness seems to challenge our second premise. How can we infer future price movements with such evidence? Much of technical analysis does not address this question. Indeed, the answer to it has to come from a quantitative science. The Stock Trends model attempts to reconcile with randomness by assuming it implicitly. It does that by inferring a normal population distribution. From this distribution we can calculate statements of probability.</div>
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Ultimately, the goal of this analysis is to provide a new ranking system. The system will allow us to evaluate existing Stock Trends reports like the Picks of the Week, or the Newly Weak Bearish reports. New reports can also be generated. These will highlight select stocks and ETFs with the highest probability of generating returns that are better than those expected for randomly chosen stocks.</div>
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Below is a heatmap and associated table of the current top 30 ‘select’ stocks based on this ranking system. The colour coding shows us which have higher mean returns relative to the expected returns of randomly selected stocks for 4-week, 13-week, and 40-week periods. The scale of green intensifies as the mean return exceeds the period base return (4-week: 0%, 13-week: 2.19%, 40-week: 6.45%). Mean returns in the immediate area around the period base return are yellow, while mean returns below the period base returns intensify in redness as they sub-perform (this group is comprised of superior performers, so red codes will be largely absent).</div>
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To qualify for this select list the lower band of the 95% confidence interval of the population mean must exceed the mean returns of randomly selected stocks. Statistically this tells us that we are confident that there is only a 2.5% chance that the mean return of the inferred population is below the period base return. From this group we derive the normal probability distribution using the population mean and standard deviation, and then calculate the probability that a return will exceed the period base return. Stocks are ranked in descending order of the probability they will out-perform the 13-week base period return.</div>
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Stock Trends Inference Model</h2>
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Current Select stocks</h2>
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<img alt="" height="1500" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/20140303_heatmap_top.png" width="625" /></div>
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<table><tbody>
<tr bgcolor="#009900"><td><h4>
Trend</h4>
</td><td><h4>
Issue</h4>
</td><td><h4 align="center">
Price($)</h4>
</td><td><h4 align="center">
% chg</h4>
</td><td><h4 align="center">
Vol(00s)</h4>
</td><td><h4>
</h4>
</td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CMG-T&s=3&p=6">Computer Modelling Group (CMG)</a></td><td><div align="right">
30.04</div>
</td><td><div align="right">
2.0</div>
</td><td><div align="right">
2111</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CSTE-Q&s=3&p=6">Caesar Stone Sdot Yam (CSTE)</a></td><td><div align="right">
59.12</div>
</td><td><div align="right">
9.9</div>
</td><td><div align="right">
13290</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=GGN-A&s=3&p=6">GAMCO Global Gold, Nat. Res. (GGN)</a></td><td><div align="right">
10.11</div>
</td><td><div align="right">
0.3</div>
</td><td><div align="right">
25165</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ETP-N&s=3&p=6">Energy Transfer Partners LP (ETP)</a></td><td><div align="right">
55.53</div>
</td><td><div align="right">
3.3</div>
</td><td><div align="right">
52522</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=KS-N&s=3&p=6">KapStone Paper and Packaging (KS)</a></td><td><div align="right">
31.79</div>
</td><td><div align="right">
2.5</div>
</td><td><div align="right">
30209</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=LDL-N&s=3&p=6">Lydall Inc. (LDL)</a></td><td><div align="right">
20.26</div>
</td><td><div align="right">
1.2</div>
</td><td><div align="right">
2441</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=THO-T&s=3&p=6">Tahoe Resources (THO)</a></td><td><div align="right">
25.98</div>
</td><td><div align="right">
5.7</div>
</td><td><div align="right">
8695</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ITC-N&s=3&p=6">ITC Holdings (ITC)</a></td><td><div align="right">
102.60</div>
</td><td><div align="right">
-1.2</div>
</td><td><div align="right">
17922</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowDnArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ECT-N&s=3&p=6">ECA Marcellus Trust I (ECT)</a></td><td><div align="right">
8.85</div>
</td><td><div align="right">
-2.4</div>
</td><td><div align="right">
4351</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ACH-N&s=3&p=6">Aluminum Corporation of China (ACH)</a></td><td><div align="right">
8.93</div>
</td><td><div align="right">
-5.7</div>
</td><td><div align="right">
4646</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=EWC-N&s=3&p=6">iShares MSCI Canada E.T.F. (EWC)</a></td><td><div align="right">
29.18</div>
</td><td><div align="right">
0.7</div>
</td><td><div align="right">
48455</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=AWAY-Q&s=3&p=6">HomeAway, Inc. (AWAY)</a></td><td><div align="right">
45.87</div>
</td><td><div align="right">
-3.9</div>
</td><td><div align="right">
116454</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=DSCO-Q&s=3&p=6">Discovery Laboratories (DSCO)</a></td><td><div align="right">
2.64</div>
</td><td><div align="right">
3.5</div>
</td><td><div align="right">
45282</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=UEIC-Q&s=3&p=6">Universal Electronics (UEIC)</a></td><td><div align="right">
41.79</div>
</td><td><div align="right">
4.2</div>
</td><td><div align="right">
6093</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CVS-N&s=3&p=6">CVS Caremark (CVS)</a></td><td><div align="right">
73.14</div>
</td><td><div align="right">
2.7</div>
</td><td><div align="right">
276007</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=RFP-T&s=3&p=6">Resolute Forest Products (RFP)</a></td><td><div align="right">
22.78</div>
</td><td><div align="right">
-4.6</div>
</td><td><div align="right">
103</div>
</td><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowStar2.gif" /></td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=REX-N&s=3&p=6">REX American Resources (REX)</a></td><td><div align="right">
47.68</div>
</td><td><div align="right">
13.2</div>
</td><td><div align="right">
2776</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=RPG-N&s=3&p=6">Guggenheim 500 Pure Growth ETF (RPG)</a></td><td><div align="right">
74.91</div>
</td><td><div align="right">
1.5</div>
</td><td><div align="right">
9223</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ISBC-Q&s=3&p=6">Investors Bancorp (ISBC)</a></td><td><div align="right">
26.52</div>
</td><td><div align="right">
3.8</div>
</td><td><div align="right">
13827</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=JCOM-Q&s=3&p=6">j2 Global (JCOM)</a></td><td><div align="right">
51.40</div>
</td><td><div align="right">
5.0</div>
</td><td><div align="right">
18158</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CS-N&s=3&p=6">Credit Suisse Group (CS)</a></td><td><div align="right">
31.37</div>
</td><td><div align="right">
-0.3</div>
</td><td><div align="right">
49564</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-HollowUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=ONB-Q&s=3&p=6">Old National Bancorp (ONB)</a></td><td><div align="right">
14.03</div>
</td><td><div align="right">
4.7</div>
</td><td><div align="right">
36613</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=CVD-N&s=3&p=6">Covance Inc. (CVD)</a></td><td><div align="right">
103.56</div>
</td><td><div align="right">
0.8</div>
</td><td><div align="right">
15274</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=GT-Q&s=3&p=6">Goodyear Tire (GT)</a></td><td><div align="right">
26.87</div>
</td><td><div align="right">
1.2</div>
</td><td><div align="right">
201723</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=NBL-N&s=3&p=6">Noble Energy Inc. (NBL)</a></td><td><div align="right">
68.76</div>
</td><td><div align="right">
2.8</div>
</td><td><div align="right">
129630</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=EPAM-N&s=3&p=6">EPAM Systems (EPAM)</a></td><td><div align="right">
41.93</div>
</td><td><div align="right">
0.1</div>
</td><td><div align="right">
14279</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=NLP-A&s=3&p=6">NTS Realty Holdings LP (NLP)</a></td><td><div align="right">
8.43</div>
</td><td><div align="right">
0.6</div>
</td><td><div align="right">
564</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=GWRE-N&s=3&p=6">Guidewire Software (GWRE)</a></td><td><div align="right">
53.61</div>
</td><td><div align="right">
4.2</div>
</td><td><div align="right">
15058</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=TRGP-N&s=3&p=6">Targa Resources Corp. (TRGP)</a></td><td><div align="right">
96.76</div>
</td><td><div align="right">
0.8</div>
</td><td><div align="right">
13167</div>
</td><td> </td></tr>
<tr><td><img alt="" border="0" src="http://www.stocktrends.com/images/ST-SolidUpArrow2.gif" /></td><td><a href="http://www.stocktrends.com/streport.php?symbol=SYRG-A&s=3&p=6">Synergy Resources (SYRG)</a></td><td><div align="right">
10.59</div>
</td><td><div align="right">
5.2</div>
</td><td><div align="right">
30837</div>
</td><td> </td></tr>
</tbody></table>
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Below is the distribution curve of 13-week returns of stocks with similar Stock Trends indicator combinations to the current Stock Trends Report on Computer Modeling Group (CMG-T), the top stock in this select list. We can see how favourable the probabilities are for CMG to return above the base 13-week random return of 2.19%, represented by the red vertical line. </div>
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For 13-week returns distribution estimation, with 95 % confidence, the 13-wk mean return of the population of stocks with a similar Stock Trends indicator combination to CMG-T will be inside [ 5.59 %, 11.492 %].</div>
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Mean return 8.54% and standard deviation of 12.58</div>
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Assumed Normal Distribution:</div>
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<br />For 13wk CLOSE P(Return > 2.19%)= 69.32% chance CMG will outperform the base return of a randomly selected stock. In the estimated population distribution (blue normal curve), all values to the right of the vertical red line at 2.19% are positve outcomes.<br /><br />68.63% of sample returns are > 2.19%</div>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-33454917112639778312014-02-26T14:52:00.000-05:002014-02-26T19:04:57.878-05:00Technical, value-centric filters<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
Usually when analysts look at the stock market they use a framework that is either market-centric or value-centric. Sometimes these are classified as top-down or bottom-up approaches, and they apply to both fundamental and technical branches of analysis. A market-centric framework looks at the market as a whole, assesses sector strengths and drills down to find specific trading opportunities that fit the best-of-breed theme. A value-centric framework looks at individual securities, finds value (fundamental or technical) opportunities and indentifies sector themes that either support a trade or expose it as a special situation.</div>
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Stock Trends is aligned with the bottom-up approach. The screening process employed here is an attempt to isolate trend and momentum trading opportunities. Thematic discussions (which orthodox technicians sometimes cynically refer to as “bedtime stories”) may lend credence to a trade, and certainly they are great for editorial purposes, but a quantitative methodology is more concerned with the mathematical foundation of a trading model. The reasons for a trade are defined by the logic of the model, not its output.</div>
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For instance, the current market conditions seem to suggest a rotation toward precious metals stocks. Our filter reports have highlighted some prominent gold plays. Other themes are evident as well, including a rotation to the real estate sector. But the trading models propagated here don’t take methodology beyond the value metric defined by the filter reports or the statistical inference model being introduced here. Patterns evident outside the models are, for lack of a better word, extraneous.</div>
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As an investor or an investment professional you may be concerned with context. Perhaps you even need to find your own rationale for a trade that colours beyond the algorithmic pale. This is a human need. It’s why the financial sector is so good at writing bedtime stories, why talking heads on business television sound so convincing when they present such refined tableaus of the economy, of market dynamics, of monetary and fiscal policy. Whether peddling feel good market positivism or the death stare of Dr. Gloom, analysts and commentators are gifted narrators and oracles. They give investors a sense of order in the midst of chaos.</div>
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However, as an algorithmic trader it is not necessary to strive to be all-knowing of market order – or even assume that any order exists. It is only necessary to understand the assumptions and methods of your model. In the case of the Stock Trends reports, as well as the inference model elaborated on again below, the output is based on a defined methodology and its implicit assumptions.</div>
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The task of a trader to translate the model’s output into a trade setup that executes a trading plan. The Stock Trends model trading strategies are examples, but there are other trade setups that can be employed using alternate plans. At some time we’ll start looking at trade setups that involve stock options, but it is important for investors new to this approach realize that the Stock Trends methodology is more about translating technical analysis into systematic trading.</div>
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Our bottom-up value approach (“value” in the world of technical analysis is based on market price and volume parameters, not financial parameters of fundamental analysis most often referred to as “value” by the broader investment business) derives regular weekly output. Let’s look at some of these reports and see how our inference model ranks the performance probabilities of the stocks in these reports.</div>
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Below is a heatmap showing the current top Picks of the Week and gold stocks as ranked by the Stock Trends inference model (introduced in recent editorials). Of these filter groups - current Picks of the Week, and gold stocks - these stocks have the best probabilities of beating the expected returns of a randomly selected stock. We'll be moving toward creating new reports that detail these rankings for other groups, as well as reports that show over-all ranking derived from the inference model.</div>
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Picks of the Week</h2>
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<strong>NYSE</strong></h3>
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<span style="text-indent: -18pt;">1.</span><span style="font-size: 7pt; text-indent: -18pt;"> </span><span style="text-indent: -18pt;">Equity Residential (</span><a href="http://www.stocktrends.com/streport.php?symbol=EQR-N&s=3&p=6" style="text-indent: -18pt;">EQR-N</a><span style="text-indent: -18pt;">)</span></div>
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2.<span style="font-size: 7pt;"> </span>Noranda Aluminum Holding (<a href="http://www.stocktrends.com/streport.php?symbol=NOR-N&s=3&p=6">NOR-N</a>)</div>
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3.<span style="font-size: 7pt;"> </span>ProShares Ultra Real Estate E.T.F. (<a href="http://www.stocktrends.com/streport.php?symbol=URE-N&s=3&p=6">URE-N</a>)</div>
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4.<span style="font-size: 7pt;"> </span>Endeavour Silver (<a href="http://www.stocktrends.com/streport.php?symbol=EXK-N&s=3&p=6">EXK-N</a>)</div>
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<span style="text-indent: -18pt;">5.</span><span style="font-size: 7pt; text-indent: -18pt;"> </span><span style="text-indent: -18pt;">Extra Space Storage (</span><a href="http://www.stocktrends.com/streport.php?symbol=EXR-N&s=3&p=6" style="text-indent: -18pt;">EXR-N</a><span style="text-indent: -18pt;">)</span></div>
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<strong>Nasdaq</strong></h2>
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1.<span style="font-size: 7pt;"> </span>VocalTec Communications (<a href="http://www.stocktrends.com/streport.php?symbol=CALL-Q&s=3&p=6">CALL-Q</a>)</div>
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2.<span style="font-size: 7pt;"> </span>Myriad Genetics (<a href="http://www.stocktrends.com/streport.php?symbol=MYGN-Q&s=3&p=6">MYGN-Q</a>)</div>
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3.<span style="font-size: 7pt;"> </span>Commercial Vehicle Group (<a href="http://www.stocktrends.com/streport.php?symbol=CVGI-Q&s=3&p=6">CVGI-Q</a>)</div>
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4.<span style="font-size: 7pt;"> </span>Cavium, Inc. (<a href="http://www.stocktrends.com/streport.php?symbol=CAVM-Q&s=3&p=6">CAVM-N</a>)</div>
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5.<span style="font-size: 7pt;"> </span>Flextronics International (<a href="http://www.stocktrends.com/streport.php?symbol=FLEX-Q&s=3&p=6">FLEX-Q</a>)</div>
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1.<span style="font-size: 7pt;"> </span>Precision Drilling (<a href="http://www.stocktrends.com/streport.php?symbol=PD-T&s=3&p=6">PD-T</a>)</div>
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2.<span style="font-size: 7pt;"> </span>MAG Silver Corp. (<a href="http://www.stocktrends.com/streport.php?symbol=MAG-T&s=3&p=6">MAG-T</a>)</div>
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<strong>Top Gold stocks (price >= $2)</strong></h2>
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1.<span style="font-size: 7pt;"> </span>Franco-Nevada (<a href="http://www.stocktrends.com/streport.php?symbol=FNV-N&s=3&p=6">FNV-N</a>)</div>
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2.<span style="font-size: 7pt;"> </span>Gold Resource (<a href="http://www.stocktrends.com/streport.php?symbol=GORO-A&s=3&p=6">GORO-A</a>)</div>
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3.<span style="font-size: 7pt;"> </span>DRDGOLD Ltd. (<a href="http://www.stocktrends.com/streport.php?symbol=DRD-N&s=3&p=6">DRD-N</a>)</div>
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4.<span style="font-size: 7pt;"> </span>Iamgold Corp. (<a href="http://www.stocktrends.com/streport.php?symbol=IAG-N&s=3&p=6">IAG-N</a>)</div>
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5.<span style="font-size: 7pt;"> </span>Virginia Mines (<a href="http://www.stocktrends.com/streport.php?symbol=VGQ-T&s=3&p=6">VGQ-T</a>)</div>
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<strong>Gold stocks (all):</strong></h2>
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1.<span style="font-size: 7pt;"> </span>Crocodile Gold (<a href="http://www.stocktrends.com/streport.php?symbol=CRK-T&s=3&p=6">CRK-T</a>)</div>
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2.<span style="font-size: 7pt;"> </span>Banro Corp. (<a href="http://www.stocktrends.com/streport.php?symbol=BAA-T&s=3&p=6">BAA-T</a>)</div>
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3.<span style="font-size: 7pt;"> </span>Gogold Resources (<a href="http://www.stocktrends.com/streport.php?symbol=GGD-T&s=3&p=6">GGD-T</a>)</div>
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4.<span style="font-size: 7pt;"> </span>Starcore International Mines (<a href="http://www.stocktrends.com/streport.php?symbol=SAM-T&s=3&p=6">SAM-T</a>)</div>
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5.<span style="font-size: 7pt;"> </span>Golden Star Resources (<a href="http://www.stocktrends.com/streport.php?symbol=GSS-A&s=3&p=6">GSS-A</a>)</div>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-90939117189415575682014-02-12T12:26:00.000-05:002014-02-13T23:41:51.055-05:00The random outcome benchmarkBehind all of the statistical modeling Stock Trends is gradually unveiling there is an attempt to break down the weekly data reports into probability distributions that give the trader an estimate of the odds of success. Success is measured in terms of relative performance against the expected returns of a randomly selected stock. This benchmark needs elaboration.<br />
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The Stock Trends inference model defines the randomness of the market in a universal way - returns are not time specific. That means that we are referring to outcomes across all time frames, all markets, and all events. We are referring to a true population of returns.<br />
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This differs from the subjectively influenced range of outcomes that we see at any moment in time. For instance, at this moment in time the market might appear to present a certain bullish trend and typical analysis approaches build in expectations of price movement associated with aspects of the analysis (chart patterns, fundamental ratios, etc.). Those subjective evaluations of potential outcomes (whether the analysis reveals a highly bearish or highly bearish scenario) generally present only a subset of universal returns. These potential outcomes are rooted in expectations of the moment.<br />
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The Stock Trends inference model's random benchmark looks at all returns possible. Those returns are derived from every market type, including markets of ‘irrational exuberance’ as well as the gravest financial meltdown. Universal returns are market agnostic – they are not framed by the boundaries of the moment. That is true randomness. We do not know what to expect. Anything is possible.<br />
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When we look at large samples random returns match the long-term market expectations – basically 8 percent annually. Actually, the expected mean annual return of a randomly selected stock, across time, is about 8.6%. Taking period returns of approximately 500,000 random stocks from the Stock Trends 30-year weekly data, the estimated population parameters for the following periods are derived:<br />
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<strong>4-week mean return: -0.05%, standard deviation: 33.15</strong></div>
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<strong>13-week mean return: 2.19%, standard deviation: 44.11</strong></div>
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<strong>40-week mean return: 6.45%, standard deviation: 76.81</strong></div>
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Obviously, the dispersion of results is quite wide. A random trade is as likely to be a double bagger as a money pit. Even more moderate ‘aggressive’ trading expectations are constrained by the same rules of the assumed normal distribution. Looking at the 40-week period for example, a random trade has a 48.1% chance of a return greater than 10%, but also has a 41.5% chance of a return less than -10%.<br />
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Assuming a normal distribution of returns for the population, we should be reminded that a binary implication of this is humbling for the trader. A trader’s odds of beating the mean return for any period are 1:2, or 50%. This is the situation most investors are in – they are at risk of entering the market at the worst of times or best of times depending on their investment life cycle, but will average out toward these mean values as we sample more and more trades over generations or multiple market cycles.<br />
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Most traders probably don’t think this way. Typically, they expect to be right and are trading because they believe they can do better than the market or that they are entering the market at an opportune time. After all, why trade if you can’t do better than an index fund or, alternatively, by not exposing to equity risk at all?<br />
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The Stock Trends inference model looks at trading as a kind of binary random outcome – heads to beat the market expected return, tails to do worse. Anyone who has experience in flipping coins knows that even a ‘fair’ coin can deliver an outcome – even lengthy series of outcomes - that seemingly defies the probabilities. A fair coin can deliver 10 consecutive heads (or tails) in the first 10 flips, for instance, 0.0977% of the time. More relevantly, over the span of 50 flips the probability of 10 consecutive heads (or tails) is 2% - certainly not impossible. This is known as a ‘run’, and every active trader has experience with both winning runs and losing runs. Every trading system will produce them.<br />
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Let’s look at our own Stock Trends trading systems. This is a distribution of loss runs of the Stock Trends <a href="http://www.stocktrends.com/stPortfolio.php?id=2">NYSE Portfolio #1</a> trading record:<br />
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In this trading record of 583 trades there are 4 loss runs of 10 or more (maximum loss run is 13). That would be our string of ‘tails’ on the flip of the coin. In the real world of trading that is also called a drawdown. It’s an ugly thing; and a painful one. But we can see how loss runs factor into every trading system - every active trader must try to limit them.<br />
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Limiting the extent of losses is the primary function of a money management system. Stock Trends model portfolio trading systems have a few elements of loss protection that are inherent. First, they use a method of position sizing. All trades are made with a specific dollar value. This is a topic of discussion on its own, and I have written about it in previous editorials, comparing fixed dollar trades against variable or random amounts. Another aspect of loss protection is in the exit strategies – the stop loss triggers and the indicator triggers. However, over-all trading results will markedly improve if loss runs can be minimized. That means improving your success ratio: the win/loss ratio.<br />
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The Stock Trends inference model is a quantitative method to improve an investor’s trade expectations in a world of randomness. We look for Stock Trends indicator combinations that represent our best chance of being on the right side of a coin toss.<br />
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Here is a heatmap showing the current top ranked stocks of the S&P 100 index according to the Stock Trends inference model:<br />
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<em>see Stock Trends editorial, </em><a href="http://www.stocktrends.com/show_article.php?id=75"><em>Ranking expectations</em></a><em> for explanation of the ranking.</em></div>
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Devon Energy (<a href="http://www.stocktrends.com/streport.php?symbol=DVN-N&s=3&p=6">DVN-N</a>), Nike (<a href="http://www.stocktrends.com/streport.php?symbol=NKE-N&s=3&p=6">NKE-N</a>), and Fedex (<a href="http://www.stocktrends.com/streport.php?symbol=FDX-N">FDX-N</a>) top the list of these big cap stocks.</div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-49372837699444725092014-02-03T20:28:00.000-05:002014-02-04T17:29:40.297-05:00Looking for an edge<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
The quantitative analysis presented here is an attempt to confront randomness in the stock market. Too often technical analysis is presented in a manner that misrepresents the nature of the market. In focusing on pattern recognition it is easy to make the assumption that patterns direct the market. It is a natural human condition to look for patterns and assign meaning to the chaotic world around us. However, there is danger in assuming that randomness is an unfortunate by-product of the dynamics of order. To the contrary, there are aspects of order in randomness, but we should never assume that outcomes are assured beyond the bounds of random probabilities.<br />
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That’s a tough statement to make in the investment business. There’s a lot of money staked on the idea that we can understand the market. Investors pay handsomely for that notion: management fees, time and money invested on analysis and guidance, among many costly investment expenses. Nothing is more costly, however, than the investment losses that occur because investors put capital at risk beyond the limits of random probabilities. We lose more money because we think the market has order when in fact it does not. Of course, financial institutions cannot present themselves as purveyors of randomness. That’s a business that goes by another name.<br />
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Stock Trends was designed based on the idea that we could categorize order in the stock market. Trend analysis is about assigning order to price movement. Reversion to the mean aspects of price momentum analysis has some application toward randomness, but the type of momentum analysis Stock Trends emphasizes is more aligned with harnessing forces of order evident in mass psychology. Exposing market randomness, at first glance, makes Stock Trends look like another snake oil gimmick – one of many peddled in the business of guiding investors. Is it possible to reconcile our trend analysis with the randomness of market outcomes?<br />
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The truthful answer to that question is… perhaps not. We can assume that many stakeholders with immense resources in the investment business are grappling with the same problem. There’s a big demand for quantitative analysts in the financial sector for a reason. It’s a battle with randomness that has forced the investment business toward a data science solution.</div>
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My solution for Stock Trends is to reframe our analysis question. It’s a solution that shifts our focus away from the traditional trend analysis framework – one that is based on notions illustrated by select samples of pattern evidence – to a framework that is data driven and presents all outcomes, supporting and non-supporting. Every technical analyst – honest ones, anyway – can show you ten contradicting charts for every tidy chart that illustrates a particular pattern. Our analysis question is now: what does the data really tell us? From that answer we can propose a trading strategy.<br />
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In previous editorials I’ve introduced a statistical inference model that attempts to translate sample observations into estimations of population means based on the indicator combinations presented in the Stock Trends Reports. The primary premise: that the Stock Trends indicator combinations represent distinct characteristics of a market condition. That condition is defined by the trend indicator, the length of trend category (major trend counter) and indicator (minor trend counter), the relative intermediate price momentum (Relative Strength Indicator) and weekly price performance (+/- indicator), as well as the evidence of unusual trading volume (volume indicator).<br />
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Here we are labelling across markets. The conditions of supply and demand represented in these indicator combinations are homogeneous – grouping the current market trend characteristics of iconic Apple’s big cap stock (<a href="http://www.stocktrends.com/streport.php?symbol=AAPL-Q&s=3&p=6">AAPL-Q</a>), for instance, with that of little-known W.R. Grace & Co. (<a href="http://www.stocktrends.com/streport.php?symbol=GRA-N&s=3&p=6">GRA-N</a>) on May 18, 2012.<br />
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The purpose of the inference model is to give us an estimated population mean and standard deviation from which to build a probability distribution – this on an assumption of randomness in the population of possible outcomes given the market condition (as defined by the indicator combination). Basically, we are trying to come to terms with randomness by assuming it, and looking for estimated indicator combination probability distributions that have a leg up on the expected random outcomes that include the universe of stocks.<br />
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Let’s again have a look at an example. It’s best that we look at the most easily defined Stock Trends event – the Crossover. We call this an event, but it is in reality a non-event. Strictly speaking it is a mathematical event defined by the crossing of a shorter-term (13-week) moving average trend line and a longer-term (40-week) moving average trend line – or more properly when the 13-week average share price moves either higher or lower than the 40-week average share price. This is not the kind of event that necessarily feeds back on a market for a stock, although in some circles it is promoted as such. However, it is an important event in our analysis that isolates changes in major trend category. It pinpoints when the Stock Trends parameters move between <a href="http://www.stocktrends.com/main.php?page=stBullishref.php">BULLISH</a> and <a href="http://www.stocktrends.com/main.php?page=stBearishref.php">BEARISH</a>.<br />
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Because in our inference model indicator combinations are supplemented with trend length qualifiers when querying the data for ‘like’ combinations, and the Crossovers by definition start a new trend, the Stock Trends <a href="http://www.stocktrends.com/main.php?page=stTrendCnt.php">trend counter</a> for Crossover stocks is not much use for comparison. All Crossovers have trend counters 1/1. The way I have chosen to deal with this is to ascribe the length of the previous trend category (major trend counter) when looking for similar instances of Crossover stocks. This maintains an influence of time on the grouping of trend category changes as well. This is an important note to make about the Crossover stocks analysed.<br />
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Ford Motor Co. (<a href="http://www.stocktrends.com/streport.php?symbol=F-N&s=3&p=6">F-N</a>) is currently a Bearish Crossover (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" height="11" width="12" />). It had a Bullish trend for 60 weeks, but the stock has faded off its resistance level at $18. Its Bullish trend did reward the Stock Trends <a href="http://www.stocktrends.com/stPortfolio.php?id=7">S&P 100 Bullish Crossover Portfolio</a> with a respectable 28% return. The assumed implication of a Bearish Crossovers is that it is a trade exit signal. Conversely, a Bullish Crossover is our typical trade entry signal. Accordingly, Ford was sold on the Bearish Crossover.<br />
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But what does our inference analysis really say about Crossovers? We’ve looked at some statistical analysis of that previously, but let’s evaluate how our probability guidance would have turned out for Ford’s stock over the past 30-years.<br />
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The first thing to note in the following data is that not all Ford stock's Crossovers are represented. Some of the Crossovers indicator combinations did not have a large enough sample size to complete the analysis. As such there are only 37 of the total 56 Crossovers that have occurred since 1980. A notable Crossover missing is that of May 8, 2009, a Bullish Crossover (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" height="11" width="12" />) that introduced a major Bullish trend.<br />
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Both sets of results - Bullish and Bearish Crossovers - are sorted by the average probability that the stock will return better than the expected mean random outcome - our inference model. We discover the following:<br />
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<strong>For Bullish Crossover events where the mean probability of Ford's stock (F) besting the expected return of randomly selected stocks was greater than 52%, the stock beat the expected random return 17 out of 21 period-returns (81%) [green cells represent returns that outperformed the expected random return]. This compares to 15 out of 36 (42%) when the mean probability is less <= 52%.</strong><br />
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<strong>For Bearish Crossover events where the mean probability of Ford's stock (F) besting the expected return of randomly selected stocks was less than 48%, the stock underperformed the expected random return 8 out of 9 returns (89%) [red cells represent returns that underperformed the expected random return]. This compares to 29 out of 42 (69%) when the mean probability is >= 48%.</strong><br />
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This example tells us that our chances for a successful trade - that is, doing better than the return we can expect on a randomly selected stock - are enhanced by the inference model. As the average probability (across time periods) is outside some deviation (here above 52% and below 48%) of the probability of random stocks, the chances of a trade meeting our goals improve.<br />
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This explanation may seem complicated at this time, and future editorials will provide more examples to help us grasp the meaningfulness of the inference model. In the end, investors are looking for a slight edge, even in a game of chance.<br />
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Another important result of this analysis is that sometimes the inference analysis contradicts our expectations as far as the trade implications for the Stock Trends indicator. Sometimes a Bullish Crossover, based on the specific indicator combinations, presents an estimated return below the expected return of randomly selected stocks. Sometimes a Bearish Crossover, based on specific indicator combinations, presents an estimated return above the expected return of randomly selected stocks.<br />
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An example of this is found in the analysis of the current Stock Trends indicator combination of Ford's stock (F). Here are the estimated probability distributions over the 4-week, 13-week, and 40-week periods.<br />
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For 4-week returns distribution estimation, with 95% confidence, the 4-week mean return of the population of stocks with a smilar Stock Trends indicator combination to F will be inside [0.006%, 1.692%].<br />
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Mean return 0.85 and standard deviation of 9.63<br />
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For a Normal Distribution:</div>
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<strong> For 4wk CLOSE P(R>0)=53.51</strong></div>
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For 4wk CLOSE P(R> 0)=46.49<br />
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<strong>For the 4-week period the probability of F returning better than the expected return of a randomly selected stock is 54%.</strong><br />
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<img alt="" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/F13.png" height="472" style="background-color: white; font-family: Arial, Helvetica, sans-serif;" width="640" /><span style="background-color: white; font-family: Arial, Helvetica, sans-serif;"></span><br />
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For 13-week returns distribution estimation, with 95 % confidence, the 13-week mean return of the population of stocks with a similar Stock Trends indicator combination to F will be inside [ 2.531 %, 5.434 %]<br />
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Mean return 3.98 and standard deviation of 16.42<br />
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<strong> For 13wk CLOSE P(R> 2.19)=54.35</strong></div>
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For 13wk CLOSE P(R>2.19)=45.65</div>
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52.3% of sample returns are >2.19%<br />
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<strong>For the 13-week period the probability of F returning better than the expected return of a randomly selected stock is 54%.</strong><br />
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For 40-week returns distribution estimation, with 95 % confidence, the 40-week mean return of the population of stocks with a similar Stock Trends indicator combination to F will be inside [ 5.9 %, 11.844 %]<br />
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Mean return 8.87 and standard deviation of 32.48<br />
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Normal Distribution<br />
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<strong> For 40wk CLOSE P(R> 6.45)=52.97</strong></div>
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For 40wk CLOSE P(R>6.45)=47.03<br />
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50.77% of sample returns are > 6.45%<br />
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<strong>For the 40-week period the probability of F returning better than the expected return of a randomly selected stock is 53%.</strong><br />
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The inference analysis is contradicting the trading implication of Ford's Bearish Crossover (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" height="11" width="12" />). This is the kind of information I hope to make more readily available on the Stock Trends website. </div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-77801501709958477392014-02-03T20:25:00.001-05:002014-02-04T17:31:01.941-05:00Ranking expectations<div style="background-color: white; font-family: Arial, Helvetica, sans-serif;">
All reports and trading strategies in <a href="http://www.stocktrends.com/member_subscriptions.php">Stock Trends Weekly Reporter</a> are derived from standard techniques of technical analysis. They are offered based on the premise that the indicators and chart patterns represented alert to possible trade events – either buying or selling. But because trading and investment advice is really a statement about future price movement - an uncertain domain – it should be framed as a probability statement.<br />
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The Stock Trends inference model I am developing is an attempt to do that. By looking at the results of past indicator combinations and assembling a sample space from which to estimate probable outcomes relative to probable outcomes of randomly selected stocks the model looks for a small edge in particular trend and price momentum situations. Put simply: if a trade is a toss of the coin, let’s find a 'special' coin.<br />
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First we must clarify that statements of probable outcomes enumerated here are not asserting what the actual outcome will be. That is unknown. The market could experience a massive correction, something that is in the realm of possible outcomes. Any individual stock could rally significantly… or drop precipitously. This analysis is not attempting to predict the price path of a stock. It is instead asserting that, assuming a particular distribution of the population of a sample space, a particular Stock Trends indicator combination signals a higher probability of a return greater than the one we would expect, on average, with a random stock selection. We’re playing to beat the monkey.<br />
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<strong>Ranking estimated means</strong><br />
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In this week’s Stock Trends <strong>Picks of the Week</strong> reports (across all exchanges covered) there are 52 stocks highlighted. They include U.S. healthcare stocks like Eli Lilly & Co. (<a href="http://www.stocktrends.com/streport.php?symbol=LLY-N&s=3&p=6">LLY-N</a>) and AMN Healthcare Services Inc. (<a href="http://www.stocktrends.com/streport.php?symbol=AHS-N&s=3&p=6">AHS-N</a>), technology stocks like Digimarc Corp. (<a href="http://www.stocktrends.com/streport.php?symbol=DMRC-Q&s=3&p=6">DMRC-Q</a>), CommTouch Software (<a href="http://www.stocktrends.com/streport.php?symbol=CTCH-Q&s=3&p=6">CTCH-Q</a>), as well as precious metals stocks like Silver Standard Resources (<a href="http://www.stocktrends.com/streport.php?symbol=SSO-T&s=3&p=6">SSO-T</a>) and Agnico Eagle Mines (<a href="http://www.stocktrends.com/streport.php?symbol=AEM-T&s=3&p=6">AEM-T</a>) on the TSX. All of the stocks are selected based on a filter that looks for certain combinations of Stock Trends indicators, as well as share price and trading volume requirements.<br />
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The Picks of the Week report is designed as a weekly screening tool, helping investors locate stocks and exchange traded funds that might be signaling a good entry point. Generally, good practice is to look for additional confirmation, either technical or fundamental, in assessing the selections. The Stock Trends inference model allows us to evaluate these picks in terms of the statistical performance of grouped indicator combinations. Which stocks give us our best chance for a positive outcome?<br />
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The following heatmap graph ranks the current top Picks of the Week selections based on the inference analysis. Each stock is evaluated to see how much its expected mean return is above the expected mean return of randomly selected stocks. The stocks are then ranked by the average mean return across all three periods (the “mean of means”).<br />
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The colour scheme for the heatmap is set to the market base for random returns. Values that are greater than the expected mean return of randomly selected stocks have progressively darker green boxes; values that are less than the expected mean return of randomly selected stocks have progressively darker red boxes. Values hovering near the expected mean of randomly selected stocks are yellow. The color key legend gives us this representation. It also shows a density distribution of the “mean of the means” returns.<br />
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This analysis points us toward stocks that have the best record of indicating higher probabilities of positive returns throughout a 40-week period. At the top of the list – among this week’s Picks of the Week stocks – is NovaGold Resources (<a href="http://www.stocktrends.com/streport.php?symbol=NG-A&s=3&p=6">NG-A</a>), AMN Healthcare Services (<a href="http://www.stocktrends.com/streport.php?symbol=AHS-N&s=3&p=6">AHS-N</a>), Hertz Global Holdings (<a href="http://www.stocktrends.com/streport.php?symbol=HTZ-N&s=3&p=6">HTZ-N</a>), and Alphatec Holdings (<a href="http://www.stocktrends.com/streport.php?symbol=ATEC-Q&s=3&p=6">ATEC-Q</a>).<br />
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<strong>A breakdown of the analysis – what does it tells us?</strong><br />
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Let's have a look at NovoGold Resources. Currently, it has a Bullish Crossover (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" height="11" width="12" />) trend indicator.<br />
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NovaGold’s current Stock Trends indicator combinations (*see note) generate the following distributions of returns for 4-week, 13-week, and 40-week periods:<br />
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The returns distribution of stocks that shared a similar Stock Trends indicator combination to NG (as described in <a href="http://www.stocktrends.com/show_article.php?id=74">last week’s editorial</a>) tells us that the stock will perform relative to the expected random mean return are as follows:<br />
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<strong>There is a 48.1% probability that NG will return better than the expected mean return of randomly selected stocks at the end of the coming 4-week period.</strong><br />
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<strong>There is a 57.9% probability that NG will return better than the expected mean return of randomly selected stocks at the end of the coming 13-week period.</strong><br />
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<strong>There is a 59.7% probability that NG will return better than the expected mean return of randomly selected stocks at the end of the coming 40-week period.</strong><br />
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This analysis suggests that while NG’s shorter-term expectations are not that positive; the longer-term expectations are quite positive.<br />
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The components of the Dow Jones Industrials give us another representation of this analysis. Here not all 30 stocks currently have indicator combinations with large enough sample sizes for our inference model. This week there are 23 DJI stocks to compare.<br />
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Here we see that 3M Co. (<a href="http://www.stocktrends.com/streport.php?symbol=MMM-N&s=3&p=6">MMM-N</a>) ranks highest, and that Coca-Cola (<a href="http://www.stocktrends.com/streport.php?symbol=KO-N&s=3&p=6">KO-N</a>) ranks lowest.<br />
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The returns distribution of stocks that shared a similar Stock Trends indicator combination to MMM tells us that the probabilities that the stock will perform relative to the expected random mean return are as follows:<br />
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<strong>There is a 52.5% probability that MMM will return better than the expected mean return of randomly selected stocks at the end of the coming 4-week period.</strong><br />
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<strong>There is a 56.2% probability that MMM will return better than the expected mean return of randomly selected stocks at the end of the coming 13-week period.</strong><br />
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<strong>There is a 65.95% probability that MMM will return better than the expected mean return of randomly selected stocks at the end of the coming 40-week period.</strong><br />
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The analysis tells us that 3M Co. stock is a good bet. The stock is now in its 101<sup>st</sup> week of a Stock Trends Bullish trend and looks to continue along its path. At the very least, you would, on average, be better off buying MMM than buying a random stock.<br />
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At the bottom of the Dow Jones Industrials in terms of current expectations is Coca-Cola.<br />
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The returns distribution of stocks that shared a similar Stock Trends indicator combination to KO tells us that the probabilities that the stock will perform relative to the expected random mean return are as follows:<br />
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<strong>There is a 42.6% probability that KO will return better than the expected mean return of randomly selected stocks at the end of the coming 4-week period.</strong><br />
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<strong>There is a 38.4% probability that KO will return better than the expected mean return of randomly selected stocks at the end of the coming 13-week period.</strong><br />
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<strong>There is a 43.4% probability that KO will return better than the expected mean return of randomly selected stocks at the end of the coming 40-week period.</strong><br />
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Here the analysis tells us that Coca-Cola’s stock is not a good bet. You would be better off to buy a random stock.</div>
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<em>[note: for Bullish Crossover and Bearish Crossover stocks the <a href="http://www.stocktrends.com/main.php?page=stTrendCnt.php">trend counter</a> criteria reverts to the previous week's counters. In that manner the length of the previous trend category is the pertinent comparison. This adjustment is made for queries on all Crossover combinations because the trend counters are set to 1 for Crossovers.]</em></div>
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Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-2139564351255281172014-02-03T20:23:00.001-05:002014-02-04T17:32:00.768-05:00Embracing RandomnessThe investment industry can be defined in a number of ways, usually centered on its fiduciary role of managing and allocating capital. But fundamentally it is a data driven industry. Data is its lifeblood. Data is its nervous system. Data is what gives investment life. Without data capital cannot be managed or allocated. Without data capital is locked away, unproductive and hoarded.<br />
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Instinctively, we know this. Every investor makes decisions based on available data. Remember, data comes in many forms – much of it qualitative, unstructured, and fluid. Structured quantitative data is more readily recorded, warehoused and shared. It includes macroeconomic data, financial statements, capital stock changes, insider trading reports, industry market analysis, and – most importantly for market technicians - market trading data. In all its forms this data alerts the marketplace to investment channels and helps form our investment opinions. The magnitude of this data is impossible to quantify.<br />
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With so much data available how can we achieve optimally informed investment decisions? Is a truly informed position, one that is measured from all essential data, a realistic expectation? The way many investment professionals talk about their version of data analysis you would think it is. But there are too many alternate informed decisions to know which would be the best, the most profitable. Indeed, your quest to find the most profitable data analysis has likely led you to a numerous sources of investment information.<br />
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Of course, data can also tell us whether one version of analysis is superior to another. How can we know whose opinion to heed except for their past record measured in a response variable? What returns have been generated by a system of analysis? But what if the trading record of the analyst who has the best story supporting his or her opinion is not much better than results derived from investment decisions based on specious factors, like astrology or the Super Bowl winner? What if, for all the esteemed knowledge of the most brilliant market oracles, the result does not match the billing? These questions and the quest for a more rigorously-tested approach to investing bring many serious traders to the world of quantitative analysis. It is only by examining the trading results of informed decisions that we will know whether the data analysed to make those decisions holds the key to success.<br />
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So, we’ve arrived in the world of the ‘quant’ – a world of data variables and the dynamic nature of their relationships. It’s not easy stuff. But don’t let that scare you away. Like most things quantitative analysis can be broken down to simpler elements.<br />
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Stock Trends is an excellent data source for analysis. It provides a consistent set of variables – the Stock Trends indicators – that can be measured against a response variable. The response variable can be subsequent share price changes of any time parameter. However, we will focus on end-of-period price changes for 4-week, 13-week, and 40-week periods. These time periods represent trade time frames best executed by the Stock Trends indicator analysis. These would be most effective time frames for position or swing traders.<br />
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In fact, the average holding period for Stock Trends trading strategies based on changing trend categories (like the Dow Jones Industrials Bullish Crossover Portfolio trading strategy) is around 40-weeks, while the average holding period in more sensitive Stock Trends trading strategies tends to be lower – between 7 to 10 weeks. Typically, we will be focused more on the 13-week time frame (3-months) as the most pertinent time frame, but both the 4-week and 40-week time frames are of interest, too.<br />
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Regardless of time frame of our quantitative analysis of Stock Trends, we will be most interested in finding opportunities to trade where the probabilities of a market outcome are better than that exhibited by random selections. Why? Because regardless of how informed decisions are codified – every Stock Trends Pick of the Week or every Jim Cramer recommendation, for example – measured trading results of any prescribed period that follow will, given enough data, tend toward a normal distribution (bell-shaped). A normal distribution, like the one presented below, implies randomness. It has 50% of observations above its mean, and 50% below its mean.<br />
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This is an important understanding of trading. In an ideal trading system there would be a positive skew to the distribution of trading results, one with a fat tail on the right side of the distribution curve. This would represent a trading record with a significant number of ‘home runs’. For example, when you look at the published Stock Trends portfolios, in particular the <a href="http://www.stocktrends.com/stPortfolio.php?id=22">Nasdaq 100 Bullish Crossover Portfolio</a>, the positive skew is evident.<br />
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However, real world and model trading strategies can only be products of a sample of possible outcomes. No matter how successful a trading strategy appears in a sample, we know that the outcomes are not a complete representation of all outcomes – past, present and future. We cannot know precisely the shape of a population distribution, but it will tend toward a normal, bell-shaped, distribution. The shape may be “skinnier” (a higher kurtosis in statistical terminology) than the example standard normal distribution shown above, but the symmetrical aspect will be formed.<br />
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If even the best strategy, using the most essential, optimal data inputs, eventually develops a distribution of results that approximates a normal curve, and a normal curve is the expected distribution of results derived from random data inputs - what is the difference? Certainly, anyone who has done capital markets quantitative work will confront this question. That is why it is important to embrace the randomness of markets. A key to success is in understanding it.<br />
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As an example of the distribution of returns we expect from random results, here is a distribution of the 13-week returns of 1,000 randomly selected North American stocks from randomly selected dates.<br />
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<strong>How do we turn the random nature of the market into a profitable trading plan? </strong><br />
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<strong>Stock Trends can help.</strong><br />
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A key premise of technical analysis is that market valuations are subjectively determined. Buyer and seller – opposing forces – effect a market price based on the balancing of these subjective valuations. In classical market technician parlance, the market price discounts all available information. However, an interdependent set of variables add a dynamic feedback loop to this subjectively determined price – elements of time, and price change. We call this price momentum and it factors into much of the terminology of technical analysis. We’ll avoid elaborating on that, but suffice to say price change is a significant element in the subjective environment of every market and is an important variable for every buyer and seller. It results in the establishment of price trends, as well as the breaking of those trends.<br />
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The reason we study price trends – and the raison d'être for Stock Trends – is the self-fulfilling aspect of price movement. The Stock Trends indicators are variables that represent different aspects of price change, as well as time. The trend indicator categories – <a href="http://www.stocktrends.com/main.php?page=stBullishref.php">BULLISH </a>and <a href="http://www.stocktrends.com/main.php?page=stBearishref.php">BEARISH </a>– tell us about the relative price changes over a longer time frame. The Weak Bullish (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" height="14" width="16" />) and Weak Bearish (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" height="13" width="14" />) trend indicators tell us about possible changes in those long-term price trends. The Bullish Crossover (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" height="11" width="12" />) and Bearish Crossover (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" height="11" width="12" />) indicators tell us about changes in the trend categories.<br />
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Adding the element of time to the trend indicators is the trend counters (major and minor trends), both of which help characterize the assigned trends. They tell us how long a stock/ETF/index has been trending – an important variable that alerts us to concepts of trend fatigue and other psychological aspects of price movement. Market technicians use techniques that attempt to interpret time fractals; similarly Stock Trends trend counters also extend our trend analysis framework.<br />
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The <a href="http://www.stocktrends.com/main.php?page=stguidersi.php">Relative Strength indicator</a> measures price momentum relative to the price movement of the benchmark market index over a thirteen week period, while the RSI +/- indicator gives a binary representation of the relative price momentum versus the benchmark index for a one week period. Finally, the Stock Trends <a href="http://www.stocktrends.com/main.php?page=stguideVolume.php">volume indicators</a> isolate unusually high weekly trading volume.<br />
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The Stock Trends variables – most specifically the combination of these variables – provide us with a dataset that is ripe for quantitative analysis. A very simple question we can pose: what are the statistical returns that the market generates after particular combinations of Stock Trends indicators? This is the concept being developed here, and introduced in recent editorials.<br />
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Let’s look at combinations (trend indicator, RSI indicator, RSI +/- indicator, volume indicator) from the current week ended Jan17. Of over 8,000 N.A.-listed common stock, exchange-traded-funds and income trust issues (NYSE, Nasdaq, NYSE-Amex, and TSX) with Stock Trends trend indicators, there are 1,113 distinct ST indicator combinations this week. Some combinations are shared by multiple issues; some are unique. Some combinations are absent this week, but recorded in other weeks.<br />
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Each of these distinct combinations, for the most part, is repeated in the Stock Trends database of over thirty years of weekly reporting. In the database there are 16,668 distinct combinations recorded, with each combination representing a sample of what would be a larger population of distinct combinations possible. Not surprisingly, the most frequently recorded combinations are Bullish trends (<img alt="" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" height="12" width="15" />) centered around the market’s price momentum (Relative Strength Indicator near 100).<br />
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In order to more accurately find like combinations of the ST indicators, though, we add another two variables to the indicator combinations – the <a href="http://www.stocktrends.com/main.php?page=stTrendCnt.php">major and minor trend counters</a> – and group RSI values within discrete ranges. In this manner we can attempt to group each combination in a meaningful way that reflects similar qualities of trend, price momentum, and volume.<br />
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By running a query for each of the current issues with a Stock Trends trend indicator to find like observations of these indicator combinations in the historical data (about 8.4-million records) and measuring the subsequent 4-week, 13-week, and 40-week returns we can determine the statistical mean and standard deviation of those returns for each sample. Then using statistical inference methods estimate an interval for the population mean. From those intervals we can rank each indicator combination.<br />
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That’s a lot to digest. Let’s try an example.<br />
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Cigna Corp. (<a href="http://www.stocktrends.com/streport.php?symbol=CI-N">CI-N</a>) is a notable name in the health care sector. Its Stock Trends Bullish trend is now 67 weeks long and the stock is outpacing the S&P 500 index by 13% over the past 13-weeks. That’s a healthy trend.<br />
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But what does this kind of trend and price momentum tell us? What guidance do other examples give us?</div>
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A query to the Stock Trends database locates 311 other records with similar Stock Trends indicator combinations. Of those records 303 pre-date 13 weeks ago and consequently have 13-week price returns recorded. The distribution of those returns is illustrated in the graph below, represented by the green density curve. The blue lined plot is of the corresponding normal distribution of the sample.<br />
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Remember, the distribution presented represents a sample of returns. Indeed, all portfolio records are samples. Back-test to your heart’s desire – but every portfolio record is by definition only a sample of a much larger population of trades. Samples - especially relatively small samples - can be flattering, and they can be less flattering, but what we are really interested in is the population of trades that are represented by a trading strategy.<br />
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Obviously, we can never truly generate a record of this or any such population. However, using statistical inference methods we can extrapolate some important pieces of information about the population from the sample. We can estimate the mean and the standard deviation of the population from the sample.<br />
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The mean 13-week return of stocks in the CI sample is 5.43% and the standard deviation of those returns is 14.98. Those are two measurements of the distribution illustrated above that we can use to say something meaningful about the population.<br />
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<strong>For 13-week (closing price) returns estimation, with 95 % confidence, the mean (average) return of the population of stocks with a similar Stock Trends indicator combination to CI will be between 4.0 % and 6.85 %. This means we are pretty confident the mean is above the mean return of a randomly selected stock (2.19%). Also, if we accept the mean return as 5.4%, a normal distribution of the population of 13-week returns tells us that 58.6 % of returns will be above 2.19% (the 13-week mean return of randomly selected stocks).</strong><br />
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Remember, this edge is relative to the random outcome we expect, which is a 50% probability of besting the mean market random outcome in the coming 13-weeks. In an exercise of chance, an edge in positive probable outcomes is an excellent foundation for relative success. Applied to the stock market it is also effective, although success will also be a function of trading practice. In this CI example there is still a 41.4% chance the 13-week return will be less than the mean return of random results, and a possibility that the loss could be substantial. It is imperative that investors learn proper trade setups to limit losses. This analysis, like all analysis in the investment business, is a starting point. Portfolio management tools are always the key to long-term trading profitability.<br />
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Stock Trends is working toward providing this quantitative analysis on all stocks, and editorials ahead will help bring a better understanding of how to use the information.</div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0tag:blogger.com,1999:blog-20897850.post-58479598233040644432013-03-15T22:27:00.001-04:002013-03-15T22:27:21.374-04:00Trading after the signal<br />
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Slippage is an important issue for market timing investors. It is a costly consequence of almost all transaction-heavy trading plans – certainly more of a concern than commission costs. Briefly defined, slippage is the difference in the price at which you intend to buy or sell an instrument and the price at which the order is filled. There are multiple reasons for this divergence, including the time that passes between order placement and execution and the relative liquidity of the instrument. Closing the gap between your expectation of what price a stock will be bought or sold and the price you actually get filled should be an important part of your trading practice.</div>
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But what about the slippage that occurs between a market signal and trade order? This is an especially important consideration when following a system published by a market-timing advisory service. Can the rates of return achieved by a trading system be closely simulated? How does the model function in real trading? Readers here would ask how do the model Stock Trends Portfolio trading systems rate against actual or achievable trading results? The signals generated by the Stock Trends trading systems are issued after the close of trading on Friday, but the model portfolios register the Friday closing price as a transaction price. Can subscribers to the service attain the same returns registered by the model portfolios by trading post-signal in the following trading sessions?</div>
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First of all, a clear statement can be made about exactly duplicating any portfolio: It would be highly improbable that the transactions of a trading strategy can be matched. Even high frequency trading systems generating split second orders cannot be regenerated exactly in the marketplace because every fraction of a second represents a new market for an instrument. Of course, the differences in results may tend toward insignificance when the time between signal and order execution is small, but actual order regeneration at the same price is still highly improbable over numerous trades.</div>
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However, we would like to see that the results generated in a particular model are reasonably simulated in actual trading. What is acceptable in terms of variance between the model results and actual results will depend upon the over-all profitability of the system. In other words, will a trading system provide returns high enough to make the differences in actual results acceptable? If a system gives you 5% returns you won’t be too happy with a 2% difference in actual results. If the system gives you 20% overall returns, 2% slippage might be acceptable.</div>
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Periodically, I do get asked how the Stock Trends model portfolio performance holds in a post-trigger market - what would be the real trading results for subscribers who want to mimic the trading activity directed by the published strategies? The answer to that question depends on the trade efficiency of the investor and on the type of stock traded. Poor trade order practice and illiquid, volatile stocks make a bad combination.</div>
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Certainly, placing ‘market orders’ on trades in this category of stock will very often result in less than optimal results. As a point of reference I will direct readers to the Stock Trends Handbook chapter on <a href="http://www.stocktrends.com/show_handbook.php?hb_email=&id=7">executing trades</a>, but there are many other sources that can educate investors on how to properly make a trade. It is important that every investor know that regardless of their source for a trade signal – whether from an advisory service, your own analysis, or your taxi cab driver’s – the responsibility is on you to execute the trade as optimally as possible. A signal to buy or sell is not a signal to go to the market unprepared. It is essential to be tactical with every trade order.</div>
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For now, let’s assume that trade order best practice is being used. What can we learn about the differences in trading results possible for investors who go to market after the Stock Trends Portfolio trade signals are issued? I will only do analysis of this question on the weekly data series I maintain and will only seek to approximate possible results on the assumption that the trade is executed in the following week. As a result any differences that are highlighted in this analysis may in fact be lower if the trade data was for the following trading day instead of the following trading week. Nevertheless, I believe this analysis should give us a pretty good idea of what kind of replication of trading prices is likely and whether there is a significant difference in results from the model portfolios published here.</div>
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How can we truly approximate actual trades made? Even if I presented trade tickets for each trade it would not be an accurate representation of the population’s (every subscriber who transacted on the signals) trade record. It is necessary to approximate as a central measure, to estimate a price at which a transaction would have tended toward. If we take the mid-point of the range of the stock price in the following period we can estimate a central point, although without actual inter-period data to see what the distribution of prices tells us it would be an imprecise estimate. For instance, a stock may have traded mostly above the range mid-point. This analysis cannot tell us how individual stocks traded on a daily or intra-day basis.</div>
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If we take a sample (<a href="http://www.stocktrends.com/userfiles/portfolios.csv" target="_blank">data, csv</a>) of over 5,200 transactions directed by the six active Stock Trends model portfolios currently published and extract trading data for the following week after trade signals (both buy and sell), we get the following statistics of the results for post-trigger trades made at the midpoint of the weekly range :</div>
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<tr><td>Mean of the difference between the post-trigger price change (%) and the published price change (%):<a href="" id="fck_paste_padding"></a></td><td><strong>-0.18</strong><a href="" id="fck_paste_padding"></a></td></tr>
<tr><td>Median of the difference between the post-trigger price change (%) and the published price change (%):<a href="" id="fck_paste_padding"></a></td><td><strong>0.19</strong><a href="" id="fck_paste_padding"></a></td></tr>
<tr><td>Median Absolute Deviation of the price difference from the published trigger price:<a href="" id="fck_paste_padding"></a></td><td><strong>3.84</strong><a href="" id="fck_paste_padding"></a></td></tr>
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This tells us that approximately 50% of the transaction prices obtained in the post-trigger period (the following week) at the midpoint of the price range had overall trade results within the range of 3.65% lower and 4.03% higher than the published trade price changes. Roughly restated, if an investor bought or sold the stocks posted in the Stock Trends portfolio transaction reports the week following, and obtained a price near the midpoint of the weekly price range, the overall results of the trades would differ only marginally from the posted results.</div>
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Of course, there will be varying experiences on individual trades, and some traders will obtain better or worse prices than the midpoint of the range. A mythical trader who somehow managed to enter each position at the lowest price and exited at the highest price post-trigger would have experienced a 47% improvement in overall returns. Conversely, a mythical trader who somehow managed to enter each trade at the highest price and exited at the lowest price post-trigger would have experienced a 44% drop in overall returns.</div>
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These two highly improbable scenarios only serve to expose the range of experiences that are possible given the broader parameter of this analysis – that the trade takes place within the following trading week of a buy/sell signal. The ranges expressed here would likely be tighter if the analysis were done solely on trades the day following the buy/sell trigger. One hopes that the typical trader would tend toward the midpoint (although it would also be a mythical trader who makes all trades at the midpoint of a range) and the results experienced over an extended period would be in line with that published in the model portfolio reports.</div>
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This exercise serves two purposes: first it reaffirms that post-trigger trading can simulate the model performances. But more importantly, it reminds us that investors trading their own account should be certain to always engage the market tactically, use limit orders regularly, and to make every effort to exact the best price possible in every trade. </div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com1tag:blogger.com,1999:blog-20897850.post-31734075888819001872013-02-27T15:33:00.001-05:002013-02-27T15:36:45.097-05:00First steps toward quantitative trading<br />
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Finding the right combination of indicators is an important launching point for technical traders. Fine tuning different charting constructs, different momentum and trend indicators and back-testing results - that's the life of many market timers. Indeed, optimizing indicators and trading systems is a growing part of the investment business, as much with wealth management institutions as among the many retail traders who live by the success of their algorithms. In its own way, Stock Trends is part of this movement toward quantitative trading.</div>
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Most quantitative trading runs through finely tuned statistical models that guide both trading systems and portfolio management. The Stock Trends analysis is more static in terms of the indicators used, but seeks to optimize applied trading systems within the given parameters. The toolset used here is basic: a moving average study, a momentum indicator, and a volume trigger. But together these elements provide a powerful - and simple - means of engaging the market on a quantitative level. Each report Stock Trends publishes can be used within the framework of a trading system.</div>
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Optimizing results through statistical modelling is a multi-step process. The first thing that must be achieved is to understand your data. It is important to be able to describe its characteristics and to determine the independent and dependent variables. I have started that process in recent editorials by generating some statistical output of the Stock Trends data. The next step is to break down those descriptive statistics by category. Each subset of data that is pulled from the data history can be grouped and assessed for differences in results of the output variable - namely, the post-observation performance of individual stocks or ETFs, or the probable outcomes based on statistical predictive models.</div>
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With a given combination of Stock Trends variables, for instance, are there sub-groups that have different characteristics. We can seek to ask questions like: what are the differences in central tendencies and the distribution of results for low priced stocks compared to high priced stocks? Does price momentum propel small cap stocks higher compared to large cap stocks? Do certain sectors perform better for the momentum trader? There are many of similar questions we can evaluate, and all would be important ones to answer when developing a trading model.</div>
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Building on last week's editorial, let's take another sample of the Stock Trends data and see what it can tell us about different categories of stocks. Of particular interest is whether we can identify any divergence of performance statistics between stocks based on their share price and the Relative Strength indicator. The Stock Trends Picks of the Week report (available to subscribers of <a href="http://www.stocktrends.com/member_subscriptions.php">Stock Trends Weekly Reporter</a>) provides an interesting sample of the data for discovery of important relationships between the variables. Last week we looked at statistics of the entire sample; here we group the data and compare results.</div>
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Again taking a sample of Picks of the Week selections since the beginning of 2012 until February 8,2013 we can create new categories for the share price and Relative Strength indicator and group by ranges.</div>
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<strong>Share price ranges</strong><br />
$2.00 - $4.99<br />
$5.00 - $9.99<br />
$10.00 - $19.99<br />
$20.00 - $49.99<br />
$50 - max($800)</div>
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<strong>Relative Strength Indicator (RSI) ranges:</strong>100-104<br />
105-109<br />
110-119<br />
120-149<br />
150-max(800)</div>
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These groupings are arbitrary, and not based on any statistical method to create bins (groups). Nevertheless, they help us categorize the data in terms or ranges that are easy to understand.<br />
So how do the statistics for the percentage change in price (to the current end date, February 22, 2013) break out for these groupings?</div>
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Below is a table, ranked by median percentage change of the groups (median represents the middle value of observations). It gives us a breakdown of the number of observations, as well as some important metrics of central tendency and distribution of results.</div>
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<table border="0" cellpadding="0" cellspacing="0" style="background-color: white; border-collapse: collapse; font-family: Arial, Helvetica, sans-serif; width: 782px;"><colgroup><col style="width: 59pt;" width="78"></col><col span="2" style="width: 48pt;" width="64"></col><col span="9" style="width: 48pt;" width="64"></col></colgroup><tbody>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt; width: 59pt;" width="78"><strong>price group($)</strong></td><td style="width: 48pt;" width="64"><strong>RSI group</strong></td><td style="text-align: right; width: 48pt;" width="64"><strong>number</strong></td><td class="xl63" style="text-align: right; width: 48pt;" width="64"><strong>mean</strong></td><td class="xl63" style="text-align: right; width: 48pt;" width="64"><strong>standard deviation</strong></td><td class="xl63" style="text-align: right; width: 48pt;" width="64"><strong>median</strong></td><td class="xl63" style="text-align: right; width: 48pt;" width="64"><strong>trimmed mean</strong></td><td class="xl63" style="text-align: right; width: 48pt;" width="64"><strong>min</strong></td><td class="xl63" style="text-align: right; width: 48pt;" width="64"><strong>max</strong></td><td class="xl63" style="text-align: right; width: 48pt;" width="64"><strong>range</strong></td><td class="xl63" style="text-align: right; width: 48pt;" width="64"><strong>skew</strong></td><td class="xl63" style="text-align: right; width: 48pt;" width="64"><strong>kurtosis</strong></td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(20,50]</td><td>(150,800]</td><td align="right">29</td><td align="right" class="xl63">15.57</td><td align="right" class="xl63">40.68</td><td align="right" class="xl63">14.70</td><td align="right" class="xl63">14.50</td><td align="right" class="xl63">-69.60</td><td align="right" class="xl63">117.10</td><td align="right" class="xl63">186.70</td><td align="right" class="xl63">0.27</td><td align="right" class="xl63">-0.22</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(50,800]</td><td>(99,105]</td><td align="right">380</td><td align="right" class="xl63">8.40</td><td align="right" class="xl63">14.55</td><td align="right" class="xl63">8.25</td><td align="right" class="xl63">8.24</td><td align="right" class="xl63">-71.80</td><td align="right" class="xl63">102.20</td><td align="right" class="xl63">174.00</td><td align="right" class="xl63">0.39</td><td align="right" class="xl63">8.74</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(20,50]</td><td>(110,120]</td><td align="right">698</td><td align="right" class="xl63">8.70</td><td align="right" class="xl63">22.40</td><td align="right" class="xl63">7.40</td><td align="right" class="xl63">8.05</td><td align="right" class="xl63">-70.10</td><td align="right" class="xl63">172.40</td><td align="right" class="xl63">242.50</td><td align="right" class="xl63">0.97</td><td align="right" class="xl63">6.46</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(10,20]</td><td>(120,150]</td><td align="right">404</td><td align="right" class="xl63">9.46</td><td align="right" class="xl63">31.83</td><td align="right" class="xl63">7.30</td><td align="right" class="xl63">8.03</td><td align="right" class="xl63">-75.90</td><td align="right" class="xl63">138.20</td><td align="right" class="xl63">214.10</td><td align="right" class="xl63">0.65</td><td align="right" class="xl63">1.43</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(50,800]</td><td>(105,110]</td><td align="right">264</td><td align="right" class="xl63">6.99</td><td align="right" class="xl63">16.82</td><td align="right" class="xl63">6.90</td><td align="right" class="xl63">7.51</td><td align="right" class="xl63">-49.60</td><td align="right" class="xl63">58.00</td><td align="right" class="xl63">107.60</td><td align="right" class="xl63">-0.29</td><td align="right" class="xl63">0.69</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(20,50]</td><td>(105,110]</td><td align="right">561</td><td align="right" class="xl63">7.88</td><td align="right" class="xl63">17.68</td><td align="right" class="xl63">6.80</td><td align="right" class="xl63">7.53</td><td align="right" class="xl63">-57.00</td><td align="right" class="xl63">110.30</td><td align="right" class="xl63">167.30</td><td align="right" class="xl63">0.60</td><td align="right" class="xl63">4.30</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(20,50]</td><td>(99,105]</td><td align="right">701</td><td align="right" class="xl63">8.40</td><td align="right" class="xl63">15.95</td><td align="right" class="xl63">6.50</td><td align="right" class="xl63">7.59</td><td align="right" class="xl63">-50.00</td><td align="right" class="xl63">101.00</td><td align="right" class="xl63">151.00</td><td align="right" class="xl63">0.67</td><td align="right" class="xl63">3.85</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(50,800]</td><td>(110,120]</td><td align="right">311</td><td align="right" class="xl63">7.95</td><td align="right" class="xl63">19.35</td><td align="right" class="xl63">6.30</td><td align="right" class="xl63">7.54</td><td align="right" class="xl63">-44.70</td><td align="right" class="xl63">106.80</td><td align="right" class="xl63">151.50</td><td align="right" class="xl63">0.49</td><td align="right" class="xl63">2.09</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(10,20]</td><td>(110,120]</td><td align="right">420</td><td align="right" class="xl63">7.15</td><td align="right" class="xl63">26.35</td><td align="right" class="xl63">6.10</td><td align="right" class="xl63">6.55</td><td align="right" class="xl63">-98.30</td><td align="right" class="xl63">156.80</td><td align="right" class="xl63">255.10</td><td align="right" class="xl63">0.59</td><td align="right" class="xl63">3.68</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(5,10]</td><td>(105,110]</td><td align="right">118</td><td align="right" class="xl63">6.37</td><td align="right" class="xl63">30.66</td><td align="right" class="xl63">5.75</td><td align="right" class="xl63">4.66</td><td align="right" class="xl63">-91.20</td><td align="right" class="xl63">150.00</td><td align="right" class="xl63">241.20</td><td align="right" class="xl63">1.26</td><td align="right" class="xl63">5.40</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(50,800]</td><td>(120,150]</td><td align="right">110</td><td align="right" class="xl63">7.53</td><td align="right" class="xl63">25.55</td><td align="right" class="xl63">5.75</td><td align="right" class="xl63">7.38</td><td align="right" class="xl63">-55.90</td><td align="right" class="xl63">62.50</td><td align="right" class="xl63">118.40</td><td align="right" class="xl63">0.00</td><td align="right" class="xl63">-0.07</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(10,20]</td><td>(105,110]</td><td align="right">287</td><td align="right" class="xl63">6.96</td><td align="right" class="xl63">21.31</td><td align="right" class="xl63">5.20</td><td align="right" class="xl63">6.57</td><td align="right" class="xl63">-64.00</td><td align="right" class="xl63">104.10</td><td align="right" class="xl63">168.10</td><td align="right" class="xl63">0.36</td><td align="right" class="xl63">2.43</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(10,20]</td><td>(99,105]</td><td align="right">323</td><td align="right" class="xl63">6.65</td><td align="right" class="xl63">20.78</td><td align="right" class="xl63">4.80</td><td align="right" class="xl63">5.97</td><td align="right" class="xl63">-98.30</td><td align="right" class="xl63">96.40</td><td align="right" class="xl63">194.70</td><td align="right" class="xl63">0.14</td><td align="right" class="xl63">4.63</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(5,10]</td><td>(110,120]</td><td align="right">269</td><td align="right" class="xl63">9.23</td><td align="right" class="xl63">34.14</td><td align="right" class="xl63">4.30</td><td align="right" class="xl63">7.39</td><td align="right" class="xl63">-92.00</td><td align="right" class="xl63">127.10</td><td align="right" class="xl63">219.10</td><td align="right" class="xl63">0.61</td><td align="right" class="xl63">1.08</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(5,10]</td><td>(99,105]</td><td align="right">123</td><td align="right" class="xl63">5.41</td><td align="right" class="xl63">18.46</td><td align="right" class="xl63">4.10</td><td align="right" class="xl63">4.83</td><td align="right" class="xl63">-58.00</td><td align="right" class="xl63">89.40</td><td align="right" class="xl63">147.40</td><td align="right" class="xl63">0.84</td><td align="right" class="xl63">4.67</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(20,50]</td><td>(120,150]</td><td align="right">390</td><td align="right" class="xl63">6.68</td><td align="right" class="xl63">30.64</td><td align="right" class="xl63">3.75</td><td align="right" class="xl63">4.49</td><td align="right" class="xl63">-71.00</td><td align="right" class="xl63">168.10</td><td align="right" class="xl63">239.10</td><td align="right" class="xl63">1.30</td><td align="right" class="xl63">4.43</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(5,10]</td><td>(120,150]</td><td align="right">336</td><td align="right" class="xl63">3.46</td><td align="right" class="xl63">33.31</td><td align="right" class="xl63">1.20</td><td align="right" class="xl63">1.38</td><td align="right" class="xl63">-89.40</td><td align="right" class="xl63">142.60</td><td align="right" class="xl63">232.00</td><td align="right" class="xl63">1.02</td><td align="right" class="xl63">2.96</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(1.99,5]</td><td>(105,110]</td><td align="right">59</td><td align="right" class="xl63">6.96</td><td align="right" class="xl63">34.59</td><td align="right" class="xl63">0.00</td><td align="right" class="xl63">3.97</td><td align="right" class="xl63">-65.10</td><td align="right" class="xl63">114.70</td><td align="right" class="xl63">179.80</td><td align="right" class="xl63">1.01</td><td align="right" class="xl63">1.21</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(1.99,5]</td><td>(99,105]</td><td align="right">59</td><td align="right" class="xl63">5.85</td><td align="right" class="xl63">32.02</td><td align="right" class="xl63">-1.10</td><td align="right" class="xl63">1.87</td><td align="right" class="xl63">-41.70</td><td align="right" class="xl63">120.20</td><td align="right" class="xl63">161.90</td><td align="right" class="xl63">1.60</td><td align="right" class="xl63">3.13</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(1.99,5]</td><td>(110,120]</td><td align="right">147</td><td align="right" class="xl63">4.31</td><td align="right" class="xl63">44.33</td><td align="right" class="xl63">-1.80</td><td align="right" class="xl63">0.84</td><td align="right" class="xl63">-85.60</td><td align="right" class="xl63">277.70</td><td align="right" class="xl63">363.30</td><td align="right" class="xl63">2.05</td><td align="right" class="xl63">9.51</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(5,10]</td><td>(150,800]</td><td align="right">77</td><td align="right" class="xl63">5.65</td><td align="right" class="xl63">66.87</td><td align="right" class="xl63">-2.00</td><td align="right" class="xl63">-3.09</td><td align="right" class="xl63">-79.90</td><td align="right" class="xl63">352.40</td><td align="right" class="xl63">432.30</td><td align="right" class="xl63">2.38</td><td align="right" class="xl63">8.75</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(1.99,5]</td><td>(120,150]</td><td align="right">289</td><td align="right" class="xl63">7.89</td><td align="right" class="xl63">53.54</td><td align="right" class="xl63">-3.00</td><td align="right" class="xl63">1.69</td><td align="right" class="xl63">-88.80</td><td align="right" class="xl63">228.10</td><td align="right" class="xl63">316.90</td><td align="right" class="xl63">1.33</td><td align="right" class="xl63">2.46</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(10,20]</td><td>(150,800]</td><td align="right">54</td><td align="right" class="xl63">0.31</td><td align="right" class="xl63">61.09</td><td align="right" class="xl63">-4.50</td><td align="right" class="xl63">-3.51</td><td align="right" class="xl63">-95.10</td><td align="right" class="xl63">176.40</td><td align="right" class="xl63">271.50</td><td align="right" class="xl63">0.68</td><td align="right" class="xl63">0.30</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(1.99,5]</td><td>(150,800]</td><td align="right">170</td><td align="right" class="xl63">6.12</td><td align="right" class="xl63">62.25</td><td align="right" class="xl63">-4.70</td><td align="right" class="xl63">-1.92</td><td align="right" class="xl63">-84.90</td><td align="right" class="xl63">501.80</td><td align="right" class="xl63">586.70</td><td align="right" class="xl63">3.43</td><td align="right" class="xl63">22.18</td></tr>
<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt;">(50,800]</td><td>(150,800]</td><td align="right">8</td><td align="right" class="xl63">14.75</td><td align="right" class="xl63">58.73</td><td align="right" class="xl63">-14.05</td><td align="right" class="xl63">14.75</td><td align="right" class="xl63">-38.60</td><td align="right" class="xl63">109.20</td><td align="right" class="xl63">147.80</td><td align="right" class="xl63">0.59</td><td align="right" class="xl63">-1.58</td></tr>
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See full table and article at <a href="http://www.stocktrends.com/show_article.php?id=51">www.stocktrends.com</a></div>
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As with most tables, the first thing that jumps out is the extreme values. In particular, at the bottom half of the rankings are stock picks that had share prices between $2 and $5, across RSI values. The Picks of the Week report during the period was least populated by stocks in this price range. It is clearly the most variable price group, and also the poorest performing as shown by the low values for the median and trimmed mean (trimmed mean here excludes the top and bottom 10% of observations).</div>
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However, with variability also come the outliers - those observations we trim in statistical analysis. In the statistical world outliers are problematic. For some traders, it's their holy grail. It's the grand slam home run they celebrate. Here it would include the 500% gain on the November pick of Novogen Ltd. (<a href="http://www.stocktrends.com/streport.php?symbol=NVGN-Q&s=3&p=6">NVGN</a>), or the 277% gain of the January 2012 Santarus Inc. (<a href="http://www.stocktrends.com/streport.php?symbol=SNTS-Q&s=3&p=6">SNTS</a>) pick - among a number of other very profitable trades.</div>
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Nevertheless, when modeling a trading system it is always most important to understand the risk-reward balance. Attempting to capture higher returns on individual trades can be self-defeating. It is far better to model a system that keys on more predictable results, with low variability and a distribution that offers the best chance of long-term profits. The groupings in the top half of the table presented here would represent the more controlled parameters.</div>
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Below is a plot that compares the mean (average) percentage change in price for Picks of the Week in the sample (with a 95% confidence interval represented by the vertical bar). Also presented are Box Plots that give a visual representation of the distributions of the percentage change in price by Stock Trends RSI category range for each share price grouping.</div>
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Based on this sample of data we could focus on certain price and RSI ranges when evaluating the Picks of the Week report. For instance, we notice that relatively higher priced shares with high price momentum (high RSI values) performed better. More generally, though, it seems that stock picks with a share price above $20 provide the most consistent results. If we take a subset of the Picks of the Week sample with share price >= 20, we find the following distribution:</div>
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If we limit our analysis to Picks of the Week with a share price of $20 or greater, what kind of performance could we expect? Assuming that an investor could randomly select 5 different Picks of the Week over the period, the statistical breakdown is as follows:</div>
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This distribution is for 1000 random samples of 5 Picks of the Week, share price >= $20. Here we can see that the distribution is closer to a normal, bell-shaped distribution.</div>
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<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt; text-align: right; width: 59pt;" width="78"><strong>Minimum</strong></td><td style="text-align: right; width: 48pt;" width="64"><strong>1st Quartile</strong></td><td style="text-align: right; width: 48pt;" width="64"><strong>Median</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>Mean</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>3rd Quartile</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>Maximum</strong></td></tr>
<tr height="20" style="height: 15pt;"><td align="right" height="20" style="height: 15pt;">-19.5</td><td align="right">5.845</td><td align="right">12.77</td><td align="right" class="xl65">13.52</td><td align="right" class="xl65">20.76</td><td align="right" class="xl65">58.70</td></tr>
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<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt; text-align: right; width: 48pt;" width="64"><strong>number</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>mean</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>standard deviation</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>median</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>trimmed mean</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>minimum</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>maximum</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>range</strong></td><td class="xl65" style="text-align: right; width: 48pt;" width="64"><strong>skew</strong></td><td style="text-align: right; width: 48pt;" width="64"><strong>kurtosis</strong></td></tr>
<tr height="20" style="height: 15pt;"><td align="right" height="20" style="height: 15pt;">1000</td><td align="right" class="xl65">13.52</td><td align="right" class="xl65">12.01</td><td align="right" class="xl65">12.77</td><td align="right" class="xl65">13.11</td><td align="right" class="xl65">-19.50</td><td align="right" class="xl65">58.70</td><td align="right" class="xl65">78.20</td><td align="right" class="xl65">0.39</td><td align="right">0.75</td></tr>
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See full table and article at <a href="http://www.stocktrends.com/show_article.php?id=51">www.stocktrends.com</a></div>
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These descriptive statistics of categories are just a starting point. We should also take other samples of the Picks of the Week data. This simple analysis is presented to show that the RSI indicator can be modeled in different ways to optimize on specific variables. In order to develop a trading model we need to delve deeper into the variance and correlation of the Stock Trends indicators as well as other market data variables like trading volume.</div>
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This particular exercise, though, helps us look at the Picks of the Week report (which is a specific sample of stocks that exhibited a particular combination of Stock Trends indicators) more constructively in terms of quantitative trading. Although there are levels of charting analysis that help pinpoint technically attractive trades within the Picks of the Week report, systematic traders should understand the importance of discovering the variable constraints that correlate with minimizing variance in trading results. In particular, we can learn how the Stock Trends indicators can guide us toward developing effective trading systems.</div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com3tag:blogger.com,1999:blog-20897850.post-87880269152642867232013-02-20T22:42:00.000-05:002013-02-20T22:42:30.614-05:00Stock Trends Picks of the Week: a statistical look<br />
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The primary action matrix published here is the Stock Trends <strong><a href="http://www.stocktrends.com/member_subscriptions.php">Picks of the Week</a></strong> report. These reports are groups of stocks, organized by exchange, that match a defined criteria or combination of variables. Each of these observations are results (roughly stated here) of the following query: select stocks and ETFs, valued over $2, that have a Stock Trends Weak Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />) or Bullish Crossover (<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />) trend indicator, a minimum level of trading volume, and a Relative Strength indicator of at least 100. Those that are Weak Bearish must also have a high probability of being a Bullish Crossover within three weeks.</div>
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This weekly screen focuses on Weak Bearish and Bullish Crossover stocks and ETFs for a simple reason: this is the transitional trend moment where issues are theoretically primed to begin a new bullish trend. Although the parameters of Stock Trends are by definition lagging, the assumption is that the long-term trend has or will change category. Sometimes arriving late to the party, these selections still arrive in time to have the forces of trend work for the trade. That is the modus operandi for the Picks of the Week report.</div>
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However, the report does not filter down to deeper levels of categorizing observations, and as a result the list is too extensive to use without additional filtering. It is really up to the investor to match the trend qualities that are presented and the technical merit of each potential trade. That is an important question: how do you isolate the best trading opportunities from the Picks of the Week report?</div>
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The answer to that question does not come easy, and I won’t attempt to answer it in this editorial. Instead, let us just say for now that one way is to draw at random from the Picks of the Week report. Before we assign value to the report itself – never mind finding the optimal trade within the report – it is important to know how outcomes of the report stack up against random outcomes more generally. Will random selections from the Picks of the Week report yield better results than random selections from the broader population – namely, all stocks and ETFs? Surely, there should be a statistically significant difference in the two probable outcomes, otherwise the Picks of the Week report lacks credibility.</div>
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Before we look at comparisons between random sample performance of the Stock Trends Picks of the Week and from a broad sample of the data population, we can get an understanding of the central tendencies and distribution of random samples of the Picks of the Week report. For instance, if an investor simply bought 5 different random Picks of the Week selections – what would be the statistical representation of those choices? This kind of mean analysis of random samples is a common statistical method in probability models.</div>
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Let’s take a sample of the Picks of the Week report: all selections, across all exchanges, since the beginning of 2012. The sum of the weekly picks during this period is 6,599. That’s a big grouping and includes all picks right up to February 8, 2013. The inclusion of very recent Picks of the Week (those in the last month, for instance) is problematic in that it does affect central tendency and distribution of the positive returns, but not in a more significant fashion than the use of “end-of-period” returns.</div>
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There are obvious problems in measuring results for a given period. For instance, stocks may reach a high and subsequently retreat, thereby under-reporting possible results if a simple end-of-period statistic (based on the most recent closing price) is used. Also, in practical terms, stocks that have hit stop levels may have been sold before the end period, thereby reducing the amount lost on the trade. Nevertheless, we’ll simplify this analysis to make the evaluation on a very crude level. We are asking: if an investor blindly picked 5 different stocks/ETFs from the Picks of the Week reports at any point during the time frame and held them to the end-point (February 15, 2013), what is the mean return and what would the distribution of those average returns look like?</div>
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First, here is a summary table and histogram of these Picks of the Week returns (% change since selection):</div>
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<tr align="center" height="20" style="height: 15pt;"><td height="20" style="height: 15pt; width: 48pt;" width="64">Minimum value (<a href="http://www.stocktrends.com/streport.php?symbol=PSN-T&s=3&p=6">PSN-T</a>)</td><td style="width: 48pt;" width="64">1st Quartile</td><td style="width: 48pt;" width="64">Median</td><td style="width: 48pt;" width="64">Mean</td><td style="width: 48pt;" width="64">3rd Quartile</td><td style="width: 48pt;" width="64">Maximum value (<a href="http://www.stocktrends.com/streport.php?symbol=SNTS-Q&s=3&p=6">SNTS-Q</a>)</td></tr>
<tr align="center" height="20" style="height: 15pt;"><td align="center" height="20" style="height: 15pt;">-98.3</td><td align="center">-4.0</td><td align="center">6.4</td><td align="center">8.678</td><td align="center">20.0</td><td align="center">274.3</td></tr>
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<tr height="20" style="height: 15pt;"><td align="center" height="20" style="height: 15pt; width: 48pt;" width="64">number</td><td align="center" style="width: 48pt;" width="64">standard deviation</td><td align="center" style="width: 48pt;" width="64">median absolute deviation</td><td align="center" style="width: 48pt;" width="64">range</td><td align="center" style="width: 48pt;" width="64">skew</td><td align="center" style="width: 48pt;" width="64">kurtosis</td></tr>
<tr height="20" style="height: 15pt;"><td align="center" height="20" style="height: 15pt;">6599</td><td align="center">28.75</td><td align="center">17.64</td><td align="center">372.6</td><td align="center">1.33</td><td align="center">7.51</td></tr>
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<img alt="" height="398" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/pickshistogram.png" width="400" /></div>
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When we present the Stock Trends Picks of the Week, though, our expectation is not that every trade will be successful. However, we would like to see that the distribution of results is favourable. In practical terms, an appealing distribution will be asymmetrical, skewed positively with a fat tail to the right. While we can describe data with mathematical determinants that tell us of likely results, including simple measures of central tendency such as the mean and the median, in the end each pick represents a random sample of this subset of the larger population. We would want to see how these descriptive terms compare to the population itself.</div>
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We won’t make an attempt to calculate the population distribution now, but we can estimate that it would be close to a normal distribution, which would be symmetrical and possibly centered at the zero value, depending on the market’s overall direction. However, we can see that the distribution of the Picks of the Week sample is asymmetrical, that it is skewed positively – a long tail to the right. Half of the results fall with the 1<sup>st</sup> and 3<sup>rd</sup>quartile (the interquartile range)– between -4% and 20%. If an investor were to buy just one of the many selections in the Picks of the Week report since the beginning of 2011 their trade would have a greater than 95% chance of resulting in a return between -29 and 42%, which represents all those picks within 2 median absolute deviations (remember that the actual results obtained may be different in a real world scenario – profits booked at higher prices, losses capped at higher prices when a stock holding begins to retreat).</div>
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But putting all your eggs in one trade is not a trading plan sensible investors would endure. It is presumed that an investor would spread risk across trades. We should then be more interested in the distribution of average returns on samples of several picks. Let’s again assume that the investor makes a handful of trades in the period based on random selections from the reports (again, across all exchanges), and holds them until the end period. These mini-portfolio results should tell us something about the effectiveness of the Picks of the Week report.</div>
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A basic statistical method often used is sampling. By taking a random sample multiple times – actually many, many times over – we can estimate probable outcomes. Generally, this kind of resampling tends to prove a basic statistical truth: regardless of whether a statistic shows a non-normal distribution, as the sample sizes increase the statistic will tend toward a normal distribution. This is called the central limit theorem. However, let us take a first step and keep the sample size consistent with money management constraints and estimate that an investor’s portfolio would consist of at least 5 positions.</div>
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Bootstrapping 1,000 random samples (with replacement, since each of these samples is an independent portfolio) of those 5 selections from the Picks of the Week report generates the following histogram representation of the sample mean (average return of the 5 selected picks) distribution, as well as a summary table:</div>
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<tr height="20" style="height: 15pt;"><td align="center" height="20" style="height: 15pt; width: 48pt;" width="64">Minimum Value</td><td align="center" style="width: 48pt;" width="64">1st Quartile</td><td align="center" style="width: 48pt;" width="64">Median</td><td align="center" style="width: 48pt;" width="64">Mean</td><td align="center" style="width: 48pt;" width="64">3rd Quartile</td><td align="center" style="width: 48pt;" width="64">Maximum Value</td></tr>
<tr height="20" style="height: 15pt;"><td align="center" height="20" style="height: 15pt;">-25.08</td><td align="center">8.11</td><td align="center">17.77</td><td align="center">18.9</td><td align="center">28.06</td><td align="center">90.4</td></tr>
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<tr height="20" style="height: 15pt;"><td height="20" style="height: 15pt; text-align: center; width: 48pt;" width="64">number</td><td align="center" style="width: 48pt;" width="64">standard deviation</td><td align="center" style="width: 48pt;" width="64">median absolute deviation</td><td align="center" style="width: 48pt;" width="64">range</td><td align="center" style="width: 48pt;" width="64">skew</td><td align="center" style="width: 48pt;" width="64">kurtosis</td></tr>
<tr height="20" style="height: 15pt;"><td align="center" height="20" style="height: 15pt;">1000</td><td align="center">15.91</td><td align="center">14.83</td><td align="center">115.48</td><td align="center">0.58</td><td align="center">0.84</td></tr>
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<img alt="" height="399" src="http://www.stocktrends.com/userfiles/image/ArticlesImages/samplemeanshistogram.png" width="400" /></div>
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Now we see that the measures of central tendency have moved up – the mean (of the sample means) is 19% - and the distribution is closer to a normal distribution. More importantly, most of the sample means are above 3% (one absolute deviation below the median). The following box plots of the sample returns and the sample means returns give us a good graphical representation of the data.</div>
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Differences in the kernel density plots are also represented by the horizontal shape of the violin plots.</div>
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Overall, this analysis indicates the Picks of the Week reports have a good record of delivering trades with above average performance. This analysis does not go deeply enough in the data to indicate optimization and does not accurately compare against random sampling of the broad population of stocks. Nevertheless, it gives us an idea of the statistical metrics behind the performance of this particular subset of Picks of the Week selections.</div>
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I’ll be digging further into the Picks of the Week report and try to isolate various combinations of the Stock Trends variables and trading stats that change the returns distribution significantly from the sample’s distribution. There are some things we can learn about price momentum and how it delivers different results based on the sector, market capitalization or the price of the stock, and other variables. Applying more rigorous statistical analysis of the Stock Trends indicators will be the primary goal of editorials. Also, while there may be room for a return to market commentaries, I am the first to recognize there are many, many sources of “opinions” about the market direction. I’ll try to stick to quantitative trading analysis, and statistical meat here. In the end, the decision about trading is up to the investor. The best any information service can do is provide a framework for understanding the risk and rewards at hand. There is no certainty - but it’s a lot better to know your odds.</div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com4tag:blogger.com,1999:blog-20897850.post-59459115229461690862013-01-16T09:31:00.000-05:002013-03-20T09:32:51.125-04:00The truth about a Bearish trend<br />
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Technical analysts assert that the market can move in an often predictable fashion. Why else do we study the patterns on a chart if not to find occasions where a pattern could be repeated. In reality, though, the only thing predictable about the market is that it will brutally squash all those deluded enough to believe it is systematically predictable. In fact, it really doesn’t matter what investment or trading approach is applied - even the most rigorous market pricing model of cause and effect is too clumsy to time the market much better than a coin toss would (check out this forecast study: <a href="http://www.rickferri.com/blog/markets/it%E2%80%99s-official-gurus-can%E2%80%99t-accurately-predict-markets/">It's Official! Gurus Can't Accurately Predict Markets</a>). Perhaps the only real merit to all of the resources that go toward modeling the market has much more to do with risk management optimization - systems and processes that help insure against bad predictions. The best offense is a good defense.</div>
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Enough about reality and portfolio management principles, though. We’re market timing believers because 1) flipping a coin isn’t as satisfying as scientific method, and 2) even a normal probability distribution of a random statistic shows there’s room for someone in the 95<sup>th</sup> percentile. Let’s just say most market timers aspire to be positive outliers – smarter than the average investor and richer for it. Stock Trends hopefully will help you be in this “<a href="http://reference.wolfram.com/mathematica/guide/HeavyTailDistributions.html">heavy-tail</a>” company and makes a proposition that the key to improved probability of market timing success is, in fact, simplicity: define a price trend, learn how the distribution of past returns of those trends favour a trade or not, and then execute an order within a trading plan that emphasizes prudent money management. Successful market timing is not so much about being a high rolling hare; it's the parsimonious trading turtle who builds wealth.</div>
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The statistical language used here may be familiar and elementary for some, but I’ll try to keep all Stock Trends commentaries as plain and clear as possible. In the past Stock Trends editorial was largely typical technical analysis storyboarding – finding existing market trends and isolating particular stocks that are in a ‘good’ technical position for a trade. Now the emphasis will be on learning if and how the Stock Trends indicators predict probable outcomes. We will seek to quantify probable outcomes based on particular observations and the combination of variables recorded. That process of discovery will be an incremental learning exercise. Let's begin!</div>
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The Stock Trends Variables</h3>
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Stock Trends is essentially a handful of discrete and continuous variables. You can see them in every Stock Trends Report on the website. They include the trend (<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />,<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />,<img alt="" height="14" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" width="16" />,<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" width="12" />,<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />,<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />) and volume (<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST-SolidStar2.gif" width="13" />,<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST-HollowStar2.gif" width="13" />) icons, RSI values, and trend counters. I will be providing more explanatory commentary on these variables and discovering underlying relationships between the Stock Trends indicators and future price performance. After all, technical analysis may be rooted in historical data, but it is future results we would like to predict.</div>
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The most important variable is the trend indicator. The trend indicator is a factor variable with 6 possible values:</div>
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BULLISH TRENDS</h4>
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<strong>Bullish Crossover</strong> (also referred to as Newly Bullish) <img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" /></div>
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<strong>Bullish </strong>(also referred to as Strong Bullish) <img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" /></div>
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<strong>Weak Bullish</strong> <img alt="" height="14" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" width="16" /></div>
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BEARISH TRENDS</h4>
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<strong>Bearish Crossover</strong> (also referred to as Newly Bearish) <img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" width="12" /></div>
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<strong>Bearish </strong>(also referred to as Strong Bearish) <img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" /></div>
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<strong>Weak Bearish</strong> <img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" /></div>
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All issues and indexes with at least 40-weeks of trading data in its time series are assigned one of these values every week. These six indicators are defined <a href="http://www.stocktrends.com/main.php?page=stTrendsymref.php">here </a>and in the <a href="http://www.stocktrends.com/show_handbook.php?hb_email=skortje@stocktrends.ca&id=4">Stock Trends Handbook, Chapter 4 - Guide to Stock Trends Symbols and Indicators</a>.</div>
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However, the major trend categories are Bullish or Bearish. Each stock - even if it were more accurately modeled to be defined as a ‘flat trend’ – must be either Stock Trends Bullish or Stock Trends Bearish, depending upon the relationship between the 13- and 40-week average price. While there are problems that go with this binary denotation, it is a simple method of grouping our weekly observations. Once we separate observations into subsets we can begin to understand the meaningfulness of the grouping. Of course, its meaningfulness can only be evaluated if the division shows different results. In this case, does the future performance of stocks categorized as Bullish (<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />,<img alt="" height="14" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" width="16" />,<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />) differ from that of Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />,<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />,<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" width="12" />) stocks? Statistical analysis of our historical weekly data tells us the answer.</div>
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Differences in Central Tendency</h3>
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As an initial step toward understanding the relationship between the indicators and future performance let’s take a sample from 13-weeks ago – October 12. Since then the market has retreated, then rallied – with the S&P 500 index now up 3% from its level on October 12th. We can now analyze a basic statistic provided weekly by Stock Trends for all issues: the 13-week price change. How does the mean (average) price change vary for each trend variable? That is the simple question we will answer.</div>
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For reasons we will examine at another time, this analysis will be limited to issues valued at $5 and greater. Controlling for only those issues of common stock and trust units that had a trend indicator 13-weeks ago, there are 5,134 observations (we will ignore ETFs and indexes for different reasons). Of course, the range of price change is large – from a painful -92% (Petrobank Energy and Resources <a href="http://www.stocktrends.com/streport.php?symbol=PBG-T&s=3&p=6">PBG-T</a>) to 187% (Uni-Pixel <a href="http://www.stocktrends.com/streport.php?symbol=UNXL-Q&s=3&p=6">UNXL-Q</a>). The <strong>mean </strong>(average) percentage change of this sample is 4.5%. This is obviously higher than the 3% advance by the market benchmark indexes, but remember that the mean is not the only measure of central tendency. The <strong>median </strong>percentage change over this period – the value that sits in the middle of all observations – is 2.7%. Our question has to do with how one measure of the statistic (13-week price per cent change) varies between different subsets of the sample that are determined by the Stock Trends indicators.</div>
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If our binary grouping of Bullish and Bearish stocks is meaningful, there should be some variability in the median values of each group. What do we find?</div>
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Of the total 5,134 observations 3,254 (63%) had Stock Trends Bullish trend indicators – <img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />, <img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />, or <img alt="" height="14" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" width="16" />. The mean percentage change in the 13-weeks for these Stock Trends Bullish major trend stocks is 4.2% (median 2.3%), while the mean percentage change for Stock Trends Bearish major trend stocks (<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" width="12" />,<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />, or <img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />) is 5.2% (median 4.2%). Here we do observe there is a difference in the performance of the two groups (Bullish and Bearish), although it is not a result we might have expected.</div>
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Certainly, we should sample other sets of records from different times and markets to see how the two major trend categories perform relatively. Of particular interest, too, are measures of variance (like the standard deviation) that quantify the distribution of the results. But for now we’ll simply drill down into these observations from October 12 and stick with the mean statistic (average price change) as a comparison for the Stock Trends trend indicators.</div>
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Why would Bearish stocks perform better than Bullish stocks in this sample? We should have a look at the breakdown of the statistic for the six minor trend categories (<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />,<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />,<img alt="" height="14" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" width="16" />,<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" width="12" />,<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />,<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />) :</div>
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<tr><td><strong> Breakdown of minor trend categories (October 12, 2012)</strong></td></tr>
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<strong># of issues</strong></div>
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<strong>Trend category</strong></div>
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<strong>Symbol</strong></div>
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<strong>Mean</strong></div>
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<strong>Median</strong></div>
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85</div>
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Bullish Crossover</div>
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<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" /></div>
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<strong>3.8</strong></div>
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2.5</div>
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2816</div>
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Bullish</div>
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<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" /></div>
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<strong>4.0</strong></div>
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2.1</div>
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353</div>
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Weak Bullish</div>
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<img alt="" height="14" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" width="16" /></div>
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<strong>5.2</strong></div>
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4.4</div>
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53</div>
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Bearish Crossover</div>
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<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" width="12" /></div>
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<strong>1.3</strong></div>
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0.5</div>
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1342</div>
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Bearish</div>
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<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" /></div>
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<strong>5.5</strong></div>
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4.5</div>
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485</div>
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Weak Bearish</div>
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<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" /></div>
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<strong>4.7</strong></div>
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3.5</div>
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The most notable statistic here is the performance of Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />) stocks – clearly the difference maker in the results recorded in the period. During the period this particular group of stocks did better than Bullish trending stocks, and better than the Weak Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />) stocks, a group we focus on for our Stock Trends <strong>Picks of the Week </strong>selections – although a Bearish stock generally travels through a Weak Bearish category before its Bullish Crossover (<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />) and would still qualify for a <strong>Picks of the Week </strong>selection subsequent to observations on October 12. (Take note, though, of the median values and the way the differences between the mean and the median say something about the distributions of returns in each group. This is something to be evaluated at another time.)</div>
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Regardless of the reason for the impressive move of these Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />) stocks, we should break down this subgroup further to see if there are particular characteristics recorded by the other Stock Trends variables that might indicate commonality. Those characteristics would be something we would look for in other samples.</div>
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Trend Counters</h3>
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Stock Trends keeps track of the time period of the major trend – number of weeks an issue or index has been categorized in a Bullish or Bearish major trend – as well as the time period of the minor trend. (See <a href="http://www.stocktrends.com/main.php?page=stTrendCnt.php">Trend Counters</a>) The minor trend counter is the number of weeks an issue or index has been in its current trend indicator (<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />,<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />,<img alt="" height="14" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" width="16" />,<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BearishXoverSmall.gif" width="12" />,<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />,<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />). The minor trend is more sensitive to price movements and is both an indicator of strong, as well as weak and changing trends. How does the length of the major trend and minor trend indicate the post-observation results from this subset of Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />) stocks from October 12?</div>
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Separating the major trend counter into two groups offers some immediate information.</div>
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Of the 1,342 (Strong) Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />) observations, 263 had Bearish major trends over 26-weeks long. Most of the (strong) Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />) observations on October 12 had been in a Bearish major trend for less than 26-weeks.</div>
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<tr><td><strong># of issues</strong></td><td><strong>Length of major trend</strong></td><td><strong>median</strong></td><td><strong>mean</strong></td></tr>
<tr><td>263</td><td>Bearish major trend at least 26-weeks long<a href="" id="fck_paste_padding"></a></td><td>0.4</td><td>1.5</td></tr>
<tr><td>1079</td><td>Bearish major trend less than 26-weeks long<a href="" id="fck_paste_padding"></a></td><td>5.6</td><td>6.4</td></tr>
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Those results again tell us something interesting: the better performing stocks were relatively short-term Bearish stocks. The mean 13-week percentage change of Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />) stocks in a Bearish major trend for less than 26 weeks was 6.4% - much better than the 1.5% recorded by those above 26-weeks long in their Bearish major trend. </div>
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Here we see that entrenched long-term Bearish trends are not stocks that this analysis would favour, and that a contrarian long trade has long odds. And some Bearish stocks are not as entrenched in a trend as others. </div>
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That a trend ends is no surprise. They always do… eventually. Nor is it surprising that some trends are not as pronounced or as forceful as others. How can we evaluate the character of a trend? We can drill down further into the trend counters to see if there is something about the quality of these Bearish trends that perhaps would provide a cue to the change ahead.</div>
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The minor trend counter is an indicator of the quality of a long-term trend. If the minor trend counter is relatively low compared to the major trend counter, we know that the major trend has included shifts into the “Weak” iteration of the trend – here a Weak Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />) trend. A low trend counter for this group indicates a recent switch back to (Strong) Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />). This obviously shows that the long-term trend is not as entrenched as those with a minor trend counter that is relatively high.</div>
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What does the data from October 12<sup>th</sup> show? The best performance from these Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />) stocks (major trend < 26 weeks) are those that have been in a Bearish minor trend for a relatively short time.</div>
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<strong>Bearish stocks with major trend >= 26 weeks and minor Trend < 4 weeks</strong></div>
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<tr><td><strong># of issues</strong></td><td><strong>Minimum</strong></td><td><strong>Median</strong></td><td><strong>Mean</strong></td><td><strong>Maximum</strong></td></tr>
<tr><td>119</td><td>-54.0</td><td>2.0</td><td>3.3</td><td>87.4</td></tr>
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<strong>Bearish stocks with major trend >= 26 weeks and minor Trend >= 4 weeks</strong></div>
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<tr><td><strong># of issues</strong></td><td><strong>Minimum</strong></td><td><strong>Median</strong></td><td><strong>Mean</strong></td><td><strong>Maximum</strong></td></tr>
<tr><td>144</td><td>-52.5</td><td>-0.3</td><td>-0.07</td><td>66.1</td></tr>
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Bearish trends bend </h3>
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We’ve laboured through this elementary statistical exercise to illustrate that while Bearish trends do reverse, that stocks eventually complete their downward drift, transformations from bear trends are often part of a process that is akin to a bending of a line, rather than a breaking of it. Finding potential breakout stocks in the Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_BearishSmall.gif" width="14" />) group is risky, though - regardless of how statistically appealing this analysis of observations presents. This is the reason Stock Trends focuses on Weak Bearish (<img alt="" height="13" src="http://www.stocktrends.com/userfiles/image/ST_WeakBearishSmall.gif" width="14" />) and Bullish Crossover (<img alt="" height="11" src="http://www.stocktrends.com/userfiles/image/ST_BullishXoverSmall.gif" width="12" />) categories in our stock selections and trading strategies. They are transformational categories by definition. However, Weak Bullish (<img alt="" height="14" src="http://www.stocktrends.com/userfiles/image/ST_WeakBullishSmall.gif" width="16" />) stocks are also transformational and certainly of interest since many of these stocks rally back to their strong Bullish trend (<img alt="" height="12" src="http://www.stocktrends.com/userfiles/image/ST_BullishSmall.gif" width="15" />). We'll take a statistical look at that category soon.</div>
Skot Kortjehttp://www.blogger.com/profile/00681338027031381137noreply@blogger.com0