Causal Alpha: Identifying Structural Drivers of Excess Returns in the US Equity Market Using Causal Machine Learning

This study investigates the causal relationship between uncertainty shocks and factor premia in the US equity market. While traditional asset pricing models rely on correlation-based factor structures, this research argues that identifying true alpha necessitates a causal framework. Employing a novel combination of Double Machine Learning (DML) and Causal Forest algorithms on Kenneth French's five-factor data and VIX-based uncertainty shocks from ۲۰۰۱ to ۲۰۲۵, robust evidence is provided demonstrating that uncertainty shocks exert significant causal effects on factor returns. The findings reveal that the market factor (MKT-RF) and the size factor (SMB) exhibit strong negative causal responses to uncertainty shocks. In contrast, the profitability factor (RMW) demonstrates a positive and significant effect. The value (HML) and investment (CMA) factors, however, show no statistically detectable causal effect. Substantial heterogeneity in these effects is documented across high- and low-volatility regimes, with the market's negative response being substantially larger in high-uncertainty periods. From an economic significance perspective, a long-only trading strategy based on causal signals delivers a competitive Sharpe ratio of ۰.۴۳۷, with a substantially lower maximum drawdown (-۳۲.۵%) compared to the market portfolio (-۵۱.۴%). The results survive rigorous robustness checks and statistical tests, providing evidence consistent with a causal effect of uncertainty shocks on factor premia. This study contributes to the growing literature on causal machine learning in finance and offers practical implications for risk management and portfolio construction.

کلیدواژه ها:

Causal Alpha ، Uncertainty Shocks ، Double Machine Learning ، Causal Forest ، Fama-French Factors