用格雷厄姆价值投资法则驯服复杂模型，降低股市风险
Quant Convergence: Bridging Classical Value Investing and Modern Factor Models for Systematic Equity Selection
- 用格雷厄姆规则做低通滤波，过滤机器学习模型的噪声
- 纯格雷厄姆模型4年收益232.13%，最大回撤仅34.53%
- 混合模型兼顾动量与安全边际，适合稳健量化选股
现代金融高度依赖复杂的机器学习模型挖掘股市模式，但这些模型常因过度拟合短期市场噪音而失效。本研究测试了本杰明·格雷厄姆的经典价值投资法则是否可作为数学上的“低通滤波器”来约束现代模型。我们构建了三类特征：纯格雷厄姆规则、现代市场因子、以及两者的混合，并在20年S&P 500数据上，用XGBoost和AutoGluon等复杂模型进行验证。采用严格买入持有策略，测试期为2022年3月至2026年3月。结果显示，复杂模型虽有222.68%高收益，却在市场崩盘前买入高波动科技股，导致39.78%大幅回撤。相比之下，纯格雷厄姆随机森林实现最高收益232.13%，风险更低（Calmar比率1.38）。混合随机森林模型取得202.91%收益，最大回撤最低（34.53%）。研究表明，格雷厄姆的‘安全边际’并非过时，而是有效防范现代AI过度冒险的关键机制。
原文摘要 · Abstract (English)
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 Ultimately, this research proves that Graham's "margin of safety" isn't outdated; it is actually a highly effective way to prevent modern AI from taking on too much risk.
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