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Stock Trends: Articles
May  01,  2019
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Trading weekly alpha patterns
-by  Skot Kortje, Stock Trends Analyst

The Stock Trends RSI +/- Pattern Analysis Model has been generating weekly probability analysis of the named sample space for thousands of stocks and ETFs over the past 5-years. The model measures weekly alpha (market outperformance) for North American equities and generates statistical inference of expected returns from the pattern portfolios. The end result is a Profit Factor for a given pattern which short-term traders can use in their trading strategies and trade setups. Let’s review the model again.

The Stock Trends RSI +/- indicator is a simple binary indicator of market outperformance or market underperformance on a given week. If a stock issue or ETF has outperformed the market benchmark - the S&P 500 Index for the U.S. market and the S&P/TSX Composite Index for the TSX - a plus sign (+) is assigned. If the stock or ETF underperforms the benchmark a minus sign (-) is assigned. This indicator is described in the Relative Strength Indicator section of the Guide to the Stock Trends Indicators.

Here is a sample history of the indicator for Ford Motor Company (F):

The RSI +/- Pattern Analysis Model asks the question: in a given trend category, does the pattern of weekly market over/under performance indicate future market over/under performance and what is the expected return of the overperformance/underperformance? If a stock outperformed the market last week, will it outperform the market this week? If it outperformed the market for four consecutive weeks, what is the probability it will outperform the market in the following week? If so, what is the expected return? These are questions traders ask that all relate to a core gambling challenge about winning streaks. The binary nature of the Stock Trends RSI +/- indicator provides us with a sample space similar to a coin toss.

Generally, if we examine the probability of market outperformance/underperformance on a weekly basis - and this will be true for any period given enough data - there is a near 50 percent probability of either outcome. As an example, Caterpillar Inc. (CAT) has outperformed the S&P 500 index on 50.2 percent of trading weeks since March 21, 1980. Bank of America (BAC) has done so 48.8 percent of trading weeks, Walmart (WMT) 51.6%, Microsoft (MSFT) 52.5%, Intel (INTC) 51.9%, and Coca-Cola (K) 50.6%. Across all North American stocks, 48 percent of stocks, ETFs, and indices have outperformed the their relevant benchmark on a weekly basis when we look at the Stock Trends 11.25-million records of weekly market outperformance/underperformance. Although it’s not a perfect or fair coin, the binary outcome of the RSI +/- indicator tends toward the distribution of randomness.

But does the probability of outperformance vary with a given price trend? Does previous outperformance indicate future outperformance? If we have tossed three heads in a row, will the next toss yield a head? In a true random toss, of course the probabilities remain the same - 50% - regardless of the streak. But does the market present us with an opportunity to measure outperformance and identify imbalances where the the market trend tilts probable outcomes away from long-term randomness toward opportunistic trading with an unfair coin? Are there times when a pattern of outperformance/underperformance turns away from the house and more favorably to the investor?

I first introduced the RSI +/- Pattern Analysis Model in a previous editorial, but let’s look at a current example of the model at work and show how a trader can possibly use it. In the Ford (F) example shown above we can see that the most recent pattern for the RSI +/- indicators which tells when F outperformed or underperformed the S&P 500 index on each week. Since 1980, F has outperformed the market in 47.3 percent of the weekly trading. However, the RSI +/- Pattern Analysis Model puts that relative market performance metric in a context of price trend.

Currently, F is in a Stock Trends Weak Bearish () trend. It’s long-term trend category is still Bearish, but the price momentum since the stock’s low at the end of 2018 is indicating a trend reversal. F has been Weak Bearish for 4 weeks now and the stock is outperforming the S&P 500 index by 6 percent (13-week Stock Trends RSI 106) thanks to the post-earnings move last week, so we know that there is a growing buy conviction for this automotive play.

Focusing just on the current trend category, Weak Bearish, and the recent pattern of weekly outperformance/underperformance, do previous observations of similar patterns in previous Weak Bearish categories tell us that the probability of future market outperformance has been enhanced? And can it tells us what the expected return will be? The defined sample space and statistical inference provide us with the answer.

The stock has outperformed the S&P 500 index for six consecutive weeks. That’s a pretty good winning streak. The model looks to historical weekly data since 1980 looking for similar streaks where F was in a Weak Bearish trend and also had such a consecutive string of weekly market outperformance, measuring post-observation returns. Here is the result of the analysis, which subscribers to Stock Trends Weekly Reporter will find at the bottom of the Profile section of the Stock Trends Report:

The analysis is an inference model, so a minimum number of observations is required for the sample - 20. In this case, the required number of observations is met up to the 6-week pattern, which is consecutive market outperformance (++++++).


The RSI +/- Pattern Analysis shows that previous observations of one week patterns of market outperformance (+) with the stock in a Weak Bearish trend indicator numbered 189, with 128 of those observations (67.7%) followed by another week of market outperformance. Further, when this event happens the estimated mean return (average) for the population (statistical inference of the mean derived from the sample) is within a range between 4.3% and 5.9%. Of course, the flip side of the coin shows that 32.3% of the observations were followed by market underperformance the following week and that the mean return of this population is between -2.2% and -0.9%. The statistical inference of the sample and its probability space tell us that the current trend market outperformance of F indicates a positive bias toward positive returns in the following week.

Remember, the occurrence of market outperformance over all trends for F is measured at 47.7%, below the random observation marker of 50%. The 20 percent difference in probability of seeing market outperformance in F is significantly higher than the observed frequency seen broadly in over 2,000 weeks since 1980.

If we look at patterns of F’s relative performance to the market going back several weeks, the analysis shows the sample statistical inference result for the previous six weeks. It just so happens that the stock has just outperformed the market six weeks in a row. The model seeks to answer the question: what are the probabilities that it will outperform the market in week seven and what would be the expected return?

The sample of this observation, a streak of six weeks of market outperformance (++++++) where F is in a Weak Bearish trend is obviously smaller, but we can use statistical inference to estimate the population returns for both sides of the event - market overperformance and market underperformance the following week.

The analysis shows that there is a 68% probability that F will outperform the market again and that the expected return is between 3.7% and 7.1%. Alternatively, there is a 32% probability of F underperforming the market with an expected return between -7.8% and 0.3%.

How do we compare and evaluate this analysis? Like any portfolio of returns we must measure the gains relative to the pains, so to speak. Investors should be aware of the Profit Factor as one measure of this relationship. Stock Trends presents the Profit Factor for all Stock Trends trading strategies, and you can find these values on the Stock Trends strategy description pages (example Stock Trends NYSE Portfolio #1). Basically, the Profit Factor calculates the ratio of the probability of profits from a winning trade and the probability of losses from a losing trade. This ratio tells us if the rewards of being right outweigh the penalties of being wrong. The higher the Profit Factor, the more the rewards of winning trades outweigh the cost of losing trades.

Here the RSI +/- Pattern Analysis Model provides a Pattern Profit Factor. Given the observation described for F (++++++, in a Weak Bearish trend currently), the Pattern Profit Factor is 3.1. You can see how that relatively good measure stacks up against the RSI +/- Pattern of other stocks and ETFs by referring to their Stock Trends Report pages under the Profile tab (always under the Stock Trends Inference Model section).

Traders could use the RSI +/- Pattern Analysis in conjunction with the Stock Trends Inference Model, which presents longer term estimated future returns (4-week, 13-week, 40-week), to either confirm entry timing or develop other more complex options setups for short-term trades. The RSI +/- Pattern Analysis addresses weekly expected returns and probable alpha outcomes. Going long stocks and ETFs with high Pattern Profit Factors and shorting those with a low Pattern Profit Factor is a naive approach, but more refined setups are advised. The Next Level Options mentoring service, which will be introduced soon here, will provide guidelines for effective, risk managed trading.

 

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