CausalRank: Discovering Causal Factors for Cross-Sectional Stock Returns

Empirical asset pricing in the finance literature requires not only identifying numerous predictive factors but also uncovering the reliable and explainable pricing mechanisms behind return predictability. To answer this question, we propose CausalRank, a foundation model designed for parent node ranking. For a given target variable, CausalRank aims to simultaneously identify its direct causal parents from a massive pool of candidate variables and rank them based on the strength of their causal effects. Specifically, we train CausalRank on synthetic and semi-synthetic data with known causal structures and intervention effects. This training enables the model to learn transferable causal inference capabilities across various causal structures and generating functions. CausalRank distinguishes direct parent variables from correlated noncausal ones by assessing each pairwise candidate-target relationship in the context of the other candidates. This enables it to predict both whether a direct causal relationship exists and its effect. Using the U.S. stock market data, we show that the investment strategy using the characteristics identified by CausalRank can achieve a higher risk premium with lower turnover. Meanwhile, it ranks these characteristics by causal effect strength, which is crucial for evaluating a factor's role in empirical asset pricing and understanding the complexity of financial markets.