Are you a systematic investor?

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.

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.

“It’s not good enough to be anecdotal or doctrinaire when it comes to trading.”

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.

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!

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.

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 (AAPL) 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 () - gives us a factor variable for the $126.60 market price.

 

 

 

 

 

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.

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?

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.

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.

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.

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 The random outcome benchmark). 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%) .

 

 

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.

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.

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.

"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."

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.

Introducing the 'Map of Stock Trends'

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.

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.

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.

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.

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 Map of the Market.

Today I am introducing a treemap of the Stock Trends Inference Model - the Map of Stock Trends. It takes the data results from the weekly analysis, sorting 4-week and 13-week returns expectations by trend category.

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 , Weak Bullish , Bearish , Weak Bearish , Bullish Crossover , Bearish Crossover ). 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.

 

 

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.

Below are the current Map of Stock Trends 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.

 

U.S. stock exchanges - big cap stocks

Toronto Stock Exchange - big cap stocks

Stock Trends RSI +/- pattern analysis

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. The Stock Trends RSI +/- indicator 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 (-). 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 (%). 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? 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 IBM 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 INTC show a 51.6% probability of a (+) over their history, the mean value across all stocks tends to 50%. 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? 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. 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%. Introduced in last week's editorial, 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. Last week AAPL 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. 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? Here is the current RSI +/- pattern analysis of AAPL: 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 (-). 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? 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. 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 (+). 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. 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.

Data-driven technical analysis

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.

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?

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.

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:

"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 'I like pullbacks on Vipshop'

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.

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.

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.

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?

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 (FDX-N) was represented in the Stock Trends Report:

Fedex's stock is labeled as Stock Trends (strong) Bullish ( ). It has been a (strong) Bullish stock for 8-weeks, and has been categorized in a Bullish trend for 71-weeks (see trend counters). The stock has under-performed the S&P 500 index by 4% in the past 13-weeks, as indicated by the Stock TrendsRelative Strength indicator (96). Last week it also underperformed the benchmark index, as indicated by the RSI (-) sign. Finally, there is no unusual volume indicator, as defined by Stock Trends. This combination of Stock Trends indicators codifies market characteristics of Fedex's stock at this moment.

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 anassumption: 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.

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?

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.

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.

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. 

Expected 13-week returns (%) implied by the Stock Trends Inference Model can be summarized briefly:

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%.

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%.

FDX is listed in the current Stock Trends Inference Model (ST-IM) Select stocks ST Filter report. 

Stock Trends Inference Model Select stocks

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.

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 The random outcome benchmark). For instance, last week’s ST-IM report for the New York Stock Exchange includes the SPDR Retail exchange traded fund (XRT). 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.

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:

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

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”.

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.

The sample subsequent returns (4-week, 13-week, 40-week) are the statistics we measure. Here is the summary for the three periods:

For 4-week CLOSE*  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 %]

[1] "With probability of 2.5 % we will have a mean return below 1.206"

Mean return 2.74% and standard deviation of 6.94

Normal Distribution
For 4wk CLOSE P(R> 0)=65.37% probability that the 4-week return will be above the base 4-week return (0%).

57.89% of 57 sample returns are >0%

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 %]

[1] "With probability of 2.5 % we will have a mean return below 5.044"

Mean return 8.77% and standard deviation of 16.51

Normal Distribution
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%).

72.73% of 55 sample returns are >2.19%

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 %]

[1] "With probability of 2.5 % we will have a mean return below 10.272"

Mean return 17.27% and standard deviation of 30.73

Normal Distribution
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%).

57.41% of 54 sample returns are >6.45%

* Note: The Stock Trends Inference Model uses end-of-period closing price returns. See Variability of returns

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.

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.

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.  

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.

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.

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.

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?

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:

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.  

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

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:

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

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).

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.

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?

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:

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.  

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

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%.

Below is a summary of how these portfolios did relative to the benchmark indexes over these 13-week periods.

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.

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

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.

Subscribers to Stock Trends Weekly Reporter should feel quite confident in actively trading the highlighted stocks in the weekly ST-IM Select stocks report.

Learn more about the Stock Trends Inference Model at www.stocktrends.com

Select stocks

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? Now that we’ve been introduced to the elements of the model (see recent editorials), 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. 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. 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. 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:

Stock Trends inference model

Select Stocks - Top 30

Current Trend Listings of Select Top 30 stocks Trend Issue Price($) % chg Vol(00s)   Hawaiian Holdings (HA) 14.20 17.9 154139 Gold Resource (GORO) 5.52 7.2 23076 Depomed Inc. (DEPO) 13.70 13.7 159944 Gol Linhas Aereas Intelig. SA (GOL) 4.42 -9.2 48506 Fair Isaac Corporation (FICO) 53.65 -0.2 10056 Emcore Corp. (EMKR) 5.16 5.7 10057 Mkt Vectors Biotech ETF (BBH) 99.88 -2.4 10260 Bellatrix Exploration (BXE) 8.95 5.9 69745 Zix Corporation (D) (ZIXI) 4.61 2.0 13777 Quantum Fuel Systems Tech. (QTWW) 10.25 18.8 87660 Cummins (CMI) 145.62 -0.2 62808 Seadrill Partners LLC (SDLP) 31.78 1.2 5573 NASDAQ Pr Income and Growth (QQQX) 18.50 1.1 2387 Media General (MEG) 17.91 -5.6 10951 Sinovac Biotech (SVA) 6.52 -0.8 23489 Jacobs Engineering Group (JEC) 63.09 4.0 49521 Endo International plc (ENDP) 73.76 -7.6 220337 Charter Communications (CHTR) 127.00 0.2 87261 Xilinx Inc. (XLNX) 53.03 1.6 186707 AMN Healthcare Services Inc. (AHS) 14.47 3.9 16981 Air Products & Chemicals (APD) 121.67 0.3 47689 Cimarex Energy (XEC) 114.21 -1.3 60310 CUI Global (CUI) 8.73 0.0 4092 Heico Corp. (HEI) 63.35 1.9 12675 Ctrip.com International (CTRP) 52.59 -2.6 146452 Rush Enterprises (RUSHA) 29.30 2.5 5680 TRW Automotive Holdings (TRW) 82.62 0.4 44648 Anworth Mortgage Asset Co (ANH) 5.09 -1.7 77612 IGM Financial (IGM) 54.65 1.2 9237 McKesson Corp. (MCK) 182.40 3.0 67988  Subscribers to Stock Trends Weekly Reporter will find a new report listed under the header 'ST-IM Select stocks of the week' in the ST Filter Listings section.  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. 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.   For  4-week CLOSE returns distribution estimation, with  95 % confidence, the  4-week CLOSE  mean return of the population of stocks  with a similar Stock Trends indicator combination to  HA will be inside [ 1.978 %, 11.294 %] [1] "With probability of  2.5% we will have a mean return below 1.978%"  Estimated population mean return  6.64% and standard deviation of 19.85  Normal Distribution  For 4wk CLOSE P(R> 0)=63.09% probability of a return greater than the 4-week mean return of a randomly selected stock. 62.75% of sample returns are >0% 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.   For  13-week CLOSE  returns distribution estimation, with  95 % confidence, the  13-week CLOSE  mean return of the population of stocks  with a similar Stock Trends indicator combination to  HA will be inside [ 10.276 %, 33.502 %] [1] "With probability of  2.5 % we will have a mean return below 10.276%"  Estimated population mean return  21.89% and standard deviation of 46.9  Normal Distribution  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. 63.04% of sample returns are >2.19% 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.   For  40-week CLOSE  returns distribution estimation, with  95 % confidence, the  40-week CLOSE  mean return of the population of stocks  with a similar Stock Trends indicator combination to  HA will be inside [ 9.773 %, 37.583 %] [1] "With probability of  2.5 % we will have a mean return below 9.773%"  Estimated mean return  23.68% and standard deviation of 54.21  Normal Distribution  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. 55.81% of sample returns are >6.45% 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).

Picks of the Week

Current NYSE Picks

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. Current Trend Listings of NYSE Picks of the week stocks Trend Issue Price($) % chg Vol(00s)   Anglogold Ashanti Ltd. (AU) 18.62 5.9 160670 Global X Gold Explorers E.T.F. (GLDX) 16.19 5.7 5652 PetroQuest Energy (PQ) 5.17 9.3 53759 Dana Holding (DAN) 22.15 2.2 83552 Stage Stores (SSI) 24.36 23.0 54502 Weatherford (WFT) 17.07 2.4 539518 EMC Corp. (EMC) 27.04 2.5 970645 Cooper Cos. (COO) 136.48 6.5 39638 Carters Inc. (CRI) 77.48 2.9 33910 Appld Industrial Technologies (AIT) 51.66 1.2 7604 Western Gas Partners LP (WES) 64.39 1.7 10911 Service Corp. International (SCI) 19.63 5.0 111258 Berkshire Hathaway (BRK.B) 122.67 6.0 225745 Sovran Self Storage (SSS) 75.00 1.4 15939 Taiwan Semiconductor (TSM) 18.77 3.9 689911 Regency Energy Partners LP (RGP) 27.38 4.3 21744 Enbridge Inc. (ENB) 43.83 3.6 44262 Cablevision Systems (CVC) 18.15 3.1 137589 RealD Inc. (RLD) 11.50 4.1 15497 ELEMENTS Intl Commodity Agric. (RJA) 8.86 4.4 9428 Barnes & Noble (BKS) 21.20 10.7 92892 Materion Corp. (MTRN) 33.07 11.8 8897 Sprott Phys. Platinum & Palla. (SPPP) 9.82 7.0 17007 HDFC Bank (HDB) 37.33 11.1 75285 Apartment Invt & Mgmt Co. (AIV) 30.64 2.5 86576 STMicroelectronics (STM) 9.35 3.5 66572 E-TRACS UBS Long Platinum (PTM) 16.66 3.1 3118 Piedmont Natural Gas (PNY) 34.25 1.3 17919 Potash Corp. of Saskatchewan (POT) 34.69 4.2 344924 ELEMENTS Intl Commodity (RJI) 8.62 1.4 40932 Flaherty&Crumrine Pref. Secur. (FFC) 18.75 1.4 11653 Hecla Mining (HL) 3.46 2.4 237539 Ruckus Wireless, Inc. (RKUS) 15.19 8.5 103125 Westpac Banking (WBK) 30.76 2.0 6111 Wells Fargo Adv. Glb Div. Opp. (EOD) 7.80 1.2 13421 Healthcare Trust of America (HTA) 11.46 2.1 111653 Mrk Vectr Jr Gold Miner E.T.F. (GDXJ) 42.50 2.8 138210

Current Nasdaq Picks

Current TSX Picks

S & P 100 index stocks

S & P/TSX 60 index stocks