Wednesday, February 26, 2014

Technical, value-centric filters

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.
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.
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.
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.
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.
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.
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.
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.

Picks of the Week




1.       Equity Residential (EQR-N)
2.       Noranda Aluminum Holding (NOR-N)
3.       ProShares Ultra Real Estate E.T.F. (URE-N)
4.       Endeavour Silver (EXK-N)
5.       Extra Space Storage (EXR-N)


1.       VocalTec Communications (CALL-Q)
2.       Myriad Genetics (MYGN-Q)
3.       Commercial Vehicle Group (CVGI-Q)
4.       Cavium, Inc. (CAVM-N)
5.       Flextronics International (FLEX-Q)


1.       Precision Drilling (PD-T)
2.       MAG Silver Corp. (MAG-T)

Top Gold stocks (price >= $2)


1.       Franco-Nevada (FNV-N)
2.       Gold Resource (GORO-A)
3.       DRDGOLD Ltd. (DRD-N)
4.       Iamgold Corp. (IAG-N)
5.       Virginia Mines (VGQ-T)

Gold stocks (all):


1.       Crocodile Gold (CRK-T)
2.       Banro Corp. (BAA-T)
3.       Gogold Resources (GGD-T)
4.       Starcore International Mines (SAM-T)
5.       Golden Star Resources (GSS-A)

Wednesday, February 12, 2014

The random outcome benchmark

Behind 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.

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.

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.

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.

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:

4-week mean return: -0.05%, standard deviation: 33.15
13-week mean return: 2.19%, standard deviation: 44.11
40-week mean return:  6.45%, standard deviation: 76.81
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%.

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.

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?

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.

Let’s look at our own Stock Trends trading systems. This is a distribution of loss runs of the Stock Trends NYSE Portfolio #1 trading record:

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.

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.

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.

Here is a heatmap showing the current top ranked stocks of the S&P 100 index according to the Stock Trends inference model:

see Stock Trends editorial, Ranking expectations for explanation of the ranking.
Devon Energy (DVN-N), Nike (NKE-N), and Fedex (FDX-N) top the list of these big cap stocks.

Monday, February 03, 2014

Looking for an edge

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.

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.

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?

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.
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.

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).

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 (AAPL-Q), for instance, with that of little-known W.R. Grace & Co. (GRA-N) on May 18, 2012.

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.

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 BULLISH and BEARISH.

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 trend counter 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.

Ford Motor Co. (F-N) is currently a Bearish Crossover (). 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 S&P 100 Bullish Crossover Portfolio 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.

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.

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 () that introduced a major Bullish trend.


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:

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%.

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%.

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.

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.

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.

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.

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%].

 Mean return  0.85 and standard deviation of 9.63

 For a Normal Distribution:
 For 4wk CLOSE P(R>0)=53.51
 For 4wk CLOSE P(R> 0)=46.49

For the 4-week period the probability of F returning better than the expected return of a randomly selected stock is 54%.

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 %]

Mean return  3.98 and standard deviation of 16.42

For a Normal Distribution
 For 13wk CLOSE P(R> 2.19)=54.35
 For 13wk CLOSE P(R>2.19)=45.65
52.3% of sample returns are >2.19%

For the 13-week period the probability of F returning better than the expected return of a randomly selected stock is 54%.

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 %]

Mean return  8.87 and standard deviation of 32.48

Normal Distribution

 For 40wk CLOSE P(R> 6.45)=52.97
 For 40wk CLOSE P(R>6.45)=47.03

50.77% of sample returns are > 6.45%

For the 40-week period the probability of F returning better than the expected return of a randomly selected stock is 53%.

The inference analysis is contradicting the trading implication of Ford's Bearish Crossover (). This is the kind of information I hope to make more readily available on the Stock Trends website. 

Ranking expectations

All reports and trading strategies in Stock Trends Weekly Reporter 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.

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.

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.

Ranking estimated means

In this week’s Stock Trends Picks of the Week reports (across all exchanges covered) there are 52 stocks highlighted. They include U.S. healthcare stocks like Eli Lilly & Co. (LLY-N) and AMN Healthcare Services Inc. (AHS-N), technology stocks like Digimarc Corp. (DMRC-Q), CommTouch Software (CTCH-Q), as well as precious metals stocks like Silver Standard Resources (SSO-T) and Agnico Eagle Mines (AEM-T) 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.

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?

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”).

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.

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 (NG-A), AMN Healthcare Services (AHS-N), Hertz Global Holdings (HTZ-N), and Alphatec Holdings (ATEC-Q).

A breakdown of the analysis – what does it tells us?

Let's have a look at NovoGold Resources. Currently, it has a Bullish Crossover () trend indicator.

NovaGold’s current Stock Trends indicator combinations (*see note) generate the following distributions of returns for 4-week, 13-week, and 40-week periods:

The returns distribution of stocks that shared a similar Stock Trends indicator combination to NG (as described in last week’s editorial) tells us that the stock will perform relative to the expected random mean return are as follows:

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.

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.

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.

This analysis suggests that while NG’s shorter-term expectations are not that positive; the longer-term expectations are quite positive.

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.

Here we see that 3M Co. (MMM-N) ranks highest, and that Coca-Cola (KO-N) ranks lowest.

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:

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.

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.

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.

The analysis tells us that 3M Co. stock is a good bet. The stock is now in its 101st 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.

At the bottom of the Dow Jones Industrials in terms of current expectations is Coca-Cola.

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:

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.

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.

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.

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.
[note: for Bullish Crossover and Bearish Crossover stocks the trend counter 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.]

Embracing Randomness

The 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.

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.

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.

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.

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.

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.

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.

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.

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 Nasdaq 100 Bullish Crossover Portfolio, the positive skew is evident.

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.

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.

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.


How do we turn the random nature of the market into a profitable trading plan? 

Stock Trends can help.

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.

The reason we study price trends – and the raison d'ĂȘtre 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 – BULLISH and BEARISH – tell us about the relative price changes over a longer time frame. The Weak Bullish () and Weak Bearish () trend indicators tell us about possible changes in those long-term price trends. The Bullish Crossover () and Bearish Crossover () indicators tell us about changes in the trend categories.

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.

The Relative Strength indicator 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 volume indicators isolate unusually high weekly trading volume.

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.

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.

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 () centered around the market’s price momentum (Relative Strength Indicator near 100).

In order to more accurately find like combinations of the ST indicators, though, we add another two variables to the indicator combinations – the major and minor trend counters – 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.

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.

That’s a lot to digest. Let’s try an example.

Cigna Corp. (CI-N) 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.

But what does this kind of trend and price momentum tell us? What guidance do other examples give us?
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.

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.

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.

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.

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).

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.

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.