The big problem with seasonality

This is the time of year when we typically start hearing seasonal predictions for the stock market. There are a few problems with seasonality, the biggest of which is that market data is highly random, and analytical tools will tease seasonal tendencies out of randomness. Until you develop some intuition about how “seasonal” random data can look, and how volatility can distort seasonality, it is hard to truly understand the value (or lack thereof) in seasonality.

I created a short video that uses the technique of “feeding” seasonal analysis random data with a known seasonal bias. In this video, you will see that even moderate volatility completely swamps the seasonal tendency, and also creates apparent patterns where none actually exist. Consider, also, that most of the random data did not have anywhere near the degree of volatility that actual market data typically has.

I have found this technique–creating random data with known but random characteristics and then analyzing it with the tools of technical analysis–to be invaluable. It is one of the best tools I have found to build a sense of how chance can impact our trading results, and to give deep insight into how tools and indicators actually work. (See the appendix of my book for other illustrations of this same concept applied to MACD.)


Adam Grimes has over two decades of experience in the industry as a trader, analyst and system developer. The author of a best-selling trading book, he has traded for his own account, for a top prop firm, and spent several years at the New York Mercantile Exchange. He focuses on the intersection of quantitative analysis and discretionary trading, and has a talent for teaching and helping traders find their own way in the market.