Causal neglect and finance

 

Finance has been dominated by factor investing, and the set of factors identified is ever-expanding. These excesses in factor premia could be related to p-hacking and overfitting with past data; however, there is a more fundamental issue: our reliance on association rather than causal inference. When there is poor causal inference, the likelihood of misinterpretation and false inference increases. We create factor mirages. In the simplest terms, building models before theory can lead to post-test narratives. We identify relationships and then develop a narrative that can explain the findings. The line of reasoning should be from theory to testing.

How can this problem be solved? Of course, a better theory will help, but there is also a way of thinking associated with causal inference that has not been effectively used in finance. The problem starts with the econometrics of finance, which is often based on Granger causality. Finance needs to map out the network of relationships and acknowledge and account for confounders - variables that can impact both independent and dependent variables. We often discuss omitted variables, but researchers need to think more deeply about the causal links with omitted variables. Researchers must also look for colliders or variables that causally downstream from both independent and dependent variables. Greater focus needs to be placed on how the world works, rather than on how the econometrics are conducted. Choose varibales wisely before testing.

The paper "Causality and Factor Investing: A Primer" effectively describes the problem and provides guidance on how to address it. This process is not easy, but it leads to better predictive models. Do not engage in causal neglect.