Causal structures for equity risk factors evolve over time

 

There has been a strong increase in the study of causal inference in finance. Given the increase in the factor zoo and p-hacking, there is a need to go back to basics and think about causality and what it takes to find causal relationships. 

The paper, "The Evolving Causal Structure of Equity Risk Factors" focuses on using causal structure learning methods to analyze 11 risk factors in the US equity markets. The relationships have become more sparse with the market beta having a strong influence on other risk factors. Much of this research work starts with structural vector autoregressive models which look at the impact past returns on current returns to measure the impact of cross-factor causation. SVAR models can be restructured as directed acyclic graphs (DAG) that can provide useful insights on causality. Inferring causality from these insights is the objective of causal structure learning. 

The number of edges for both correlation and causal networks have fallen through time. The markets are more sparse and more focused on instantaneous relationships. The authors also find that the number of edges in the network is negatively related to VIX shocks; however, the number of edges in the causal network will increase with a volatility shock. The volatility shock is a large increase (above the 95th percentile) in VIX between the close and open values standardized. 

This work is just a start. What is more important is finding casual relationships that can be exploited for profit.