Nile Flooded Every Year, So Does This Stock

The Nile flooded every year. Ancient Egyptians didn’t predict the weather to know when to plant crops; they read the water level on stone columns called Nilometers. By trusting centuries of past data, they turned a chaotic river into a reliable food supply. Keith Kaplan, the CEO of TradeSmith, argues that the stock market functions similarly. He suggests that investors should stop trying to forecast the unpredictable future and instead use history to find dates when the odds are tilted in their favor.

Historical Accuracy in Market Data

Wall Street traders have long tracked seasonal patterns, from the “Sell in May and go away” adage to gold’s seasonal strength tied to jewelry demand in India and China. The difference today is the scale of the analysis. TradeSmith’s software processes more than 2 quintillion historical price points across roughly 5,000 stocks. The system runs millions of tests to determine if there is an optimal time of year to buy or sell individual equities. Over an 18-year period, this data-driven approach produced consistent results, turning every $10,000 invested into $85,700.

The system identifies specific “green zones” on the calendar for individual stocks. For example, shares of Boston Beer have climbed an average of 6.6% over the next 17 days every October 6 for the past 15 years, doing so in 100% of those instances. Nvidia has followed a similar pattern, rising an average of 6.5% over the next 18 days every October 23. These movements occurred regardless of earnings reports or macroeconomic news headlines, appearing consistently across both bull and bear markets. The software isolates these dates by filtering out variables like interest rate announcements and earnings outcomes.

Why Seasonality Matters

Modern investors face a barrage of information, from geopolitical conflicts like the situation in the Strait of Hormuz to the rapid developments in artificial intelligence. However, relying on predictions for these specific events is notoriously difficult. Keith Kaplan notes that even professional forecasters, such as those tracked in long-term studies by Wharton School professor Philip Tetlock, often perform no better than random chance. He suggests that attempting to predict the next geopolitical shock or Fed decision is a losing strategy for individual investors. Instead, the focus should shift to understanding when historical patterns suggest a favorable environment for specific assets.

Seasonality offers a method to handle this noise by relying on data that has existed for decades. While markets will always feel chaotic, the recurring nature of certain seasonal tendencies means that specific calendar windows can serve as a guide. By identifying these windows, investors can potentially filter out irrelevant signals and focus on periods where historical data indicates a higher probability of positive returns. The approach requires trusting the past patterns to hold rather than trying to peer into an uncertain future.