Stock Trends is distinctive - and perhaps, most effective - because it is simple. However, we are working on publishing statistical analysis of our data so that investors can execute trades with a defined positive expectation. We're keeping it simple, but the analysis will be more powerful.
Investors are always looking for an edge. More specifically, they are looking for ways to beat the market. Even if the investment climate secured tidy 8 per cent annual returns – a fairy tale now – many investors would still strive to satiate a greater appetite than the market’s benchmark performance. It is human nature to want more, to believe that we can have more. And when investors get word of some having more – of achieving much superior returns than the market inherently provides – well, their drive to share in the bounty is fast and furious. The drive to be smarter- to be better - brings forth a plethora of financial models, portfolio management practices, and trading strategies. Stock Trends, like many advisory services, exists because of this demand.
Even though the weekly analysis that Stock Trends generates is - by design - simple, it offers plenty of statistical feed for those demanding quantifiable evidence of its effectiveness in creating positive expectations for every trade. Positive expectation means that the investor has some measure of probability that a given trade will succeed. Without positive expectations of success the investor is better off throwing darts – maybe even better off paying an investment manager to lose money. At least with managed money you can always blame your advisor when things go wrong!
How can we create a trading strategy that has positive expectations? How do we measure these probabilities? These are the essential questions. Every investor should already know – through wisdom or hard-earned experience – that one cannot expect to be ‘right’ all the time. No matter how you go about it, stock picking with high degrees of success is difficult. Yes, many trades can jump off the charts and reward handsomely; but many others will defy all your best judgment. Successful traders know that in order to make their trading records profitable over long periods, sound money management principles need to be the foundation of a trading plan. Questions of how much to trade, and when to sell are as important as what to buy. The stock picker is just one aspect of the trader.
Nevertheless, finding stocks with high, or acceptably high, probability of success is important. Market-timing approaches will focus on selecting stocks with evidence of a positive correlation between current market indicators versus the empirical record of market indicators already exhibited. Stock Trends offers a wonderful opportunity to examine the statistical record of the weekly observations of the indicator variables published here. By running statistical probability analysis we will be able to more accurately represent the meaningfulness of the Stock Trends analysis. Every Stock Trends Report should also answer the question: what does this mean?
By looking back at share price performance of grouped observations – that is, stocks/ETFs that have exhibited similar Stock Trends indicators, price, and volume – we can make some representation of the probability of the share price advancing, and to what degree we might expect it to advance. The simple Stock Trends indicators provide us with a laboratory for understanding the statistical significance of the variables describing each issue reported on every week. It is not enough to say - in the way technical analysis purports in its base premise- that prices trend and these trend patterns tend to repeat themselves. We should be able to quantify these patterns. We should be able to provide a confidence level for achieving some mean (statistical average) performance level and quantify the variability of the statistic.
If that sounds Greek to you (or Swahili, if you are Greek), don’t leave quite yet. All you really want to know is: “What actions do I take?” Buy, sell, hold. Right now the Stock Trends analysis - presented either in tabular form in weekly reports, or individual records found in the Stock Trends Reports – provides a framework for market-timing trading. That is represented below.
Trend Symbols – Quick Reference
| Symbol |
Name |
Trend
Category |
13-week MA
vs.
40-week MA |
Current Price
vs
13-week MA
'Envelope' |
Trading
implication |
 |
Bullish Crossover
(New Bullish) |
Bullish |
Intersected
(above) |
Above
lower limit
(-3%) |
Buy |
 |
(Strong) Bullish |
Bullish |
Above |
Above
lower limit
(-3%) |
Buy |
 |
Weak Bullish |
Bullish |
Above |
Below
lower limit
(-3%) |
Hold/Sell |
 |
Bearish Crossover
(New Bearish) |
Bearish |
Intersected
(below) |
Below
upper limit
(+3%) |
Sell |
 |
(Strong) Bearish |
Bearish |
Below |
Below
upper limit
(+3%) |
Sell |
 |
Weak Bearish |
Bearish |
Below |
Above
upper limit
(+3%) |
Monitor/Buy |
However, we are now working toward building new dashboard tabs in the Stock Trends Reports that will present statistical probabilities of positive expectations. For instance, what is the probability of a positive gain in the upcoming 4-week period? In the next 13-weeks? Past performance does not guarantee future results, but we can give a statistical confidence level in matching the mean (average) results of the historical datasets.
Some of these models can be built off of random resampled observations. Certainly, empirical results can also be compared to random results. In fact, it is important when measuring performance of a given data set to provide a benchmark against random results. Correlation of performance results to the Stock Trends indicators must be measured against the returns on random picks. No matter how rigorous the model, we have to show the analysis beats the monkey.
We will be talking about this statistical work more going forward. Let’s hope you find it meaningful in your own investment approach. In the meantime, Stock Trends will continue to do what it has for almost two decades: give investors consistent trend and momentum reporting every week.
I very much like the systematic approach to analyzing stock data, it fits my approach.
