🤖 AI Summary
This study addresses the tendency of modern machine learning models to overfit short-term market noise in stock selection, thereby neglecting firms’ long-term intrinsic value. To bridge this gap, the authors formally operationalize Benjamin Graham’s “margin of safety” principle as a low-pass filter, integrating his classic value-investing rules with contemporary factor models to construct three distinct feature sets. Using two decades of S&P 500 data, they evaluate XGBoost, Random Forest, and AutoGluon under a four-year buy-and-hold strategy to assess out-of-sample robustness. Results demonstrate that a pure Graham-inspired Random Forest achieves a cumulative return of 232.13% and a Calmar ratio of 1.38, while the hybrid model attains a 202.91% return with the lowest drawdown (34.53%), significantly outperforming high-volatility purely AI-driven approaches.
📝 Abstract
Modern finance relies heavily on complex machine learning models to find patterns in the stock market. However, as these AI models get more complicated, they often memorize short-term market noise instead of finding companies with real, lasting value. We designed this research to test if Benjamin Graham's classic value investing rules could act as a mathematical "low-pass filter" to keep these modern models in check. We built three different sets of features - pure Graham rules, modern market factors, and a mix of both - and tested them against highly complex models (XGBoost and AutoGluon) using 20 years of S&P 500 data. By applying a strict buy-and-hold strategy over a four-year test period (March 2022 to March 2026), the results showed that more complex algorithms do not always win. While the AutoGluon model captured high returns (222.68%), it suffered a substantial 39.78% drop because it bought volatile tech stocks right before the market crashed. On the other hand, the pure Graham Random Forest achieved the highest overall return (232.13%) with much less risk (1.38 Calmar Ratio). Furthermore, the Combined Random Forest successfully mixed momentum with Graham's rules, making a 202.91% return while keeping the lowest maximum drop (34.53%) of any model tested. Ultimately, this research proves that Graham's "margin of safety" isn't outdated; it is actually a highly effective way to prevent modern AI from taking on too much risk.