The Case for Causal Factor Investing

Abstract

Researchers use factor models to obtain unbiased estimates of the premia harvested by assets exposed to certain risk characteristics. These estimates are unbiased only if the factor models are correctly specified. Choosing the correct model specification requires knowledge of the causal graph that characterizes the underlying data-generating process. However, following the current econometric canon, factor researchers choose their model specifications using associational (non-causal) arguments, such as the model’s explanatory power, instead of applying causal inference procedures, such as do-calculus. As a result, factor investing models are likely misspecified, and the estimates of risk premia are biased. This paper explains the dire consequences of factor investing’s specification errors, and calls for the need to rebuild the discipline under the more scientific foundations of causal factor investing.

Keywords: Causal inference, causal discovery, confounder, collider, factor investing, p-hacking, underperformance, systematic losses

JEL Classification: G0, G1, G2, G15, G24, E44.