The evolving causal structure of equity risk factors

Abstract

In recent years, multi-factor strategies have gained increasing popularity in the financial industry, as they allow investors to have a better understanding of the risk drivers underlying their portfolios. Moreover, such strategies promise to promote diversification and thus limit losses in times of financial turmoil. However, recent studies have reported a significant level of redundancy between these factors, which might enhance risk contagion among multi-factor portfolios during financial crises. Therefore, it is of fundamental importance to better understand the relationships among factors.Empowered by recent advances in causal structure learning methods, this paper presents a study of the causal structure of financial risk factors and its evolution over time. In particular, the data we analyze covers 11 risk factors concerning the US equity market, spanning a period of 29 years at daily frequency.Our results show a statistically significant sparsifying trend of the underlying causal structure. However, this trend breaks down during periods of financial stress, in which we can observe a densification of the causal network driven by a growth of the out-degree of the market factor node. Finally, we present a comparison with the analysis of factors cross-correlations, which further confirms the importance of causal analysis for gaining deeper insights in the dynamics of the factor system, particularly during economic downturns.Our findings are especially significant from a risk-management perspective. They link the evolution of the causal structure of equity risk factors with market volatility and a worsening macroeconomic environment, and show that, in times of financial crisis, exposure to different factors boils down to exposure to the market risk factor.

Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information

“…The following are examples of applications of statistical inference in the financial field. D'Acunto et al [32] used VAR-LiNGAM, which incorporates time series into the Linear Non-Gaussian Acyclic Model (LiNGAM), a semiparametric causal inference algorithm, to reveal the causal structure of risk factors in stocks. Ohmura [33] analyzed the relationship between the stock market and political support using VAR-LiNGAM.…”

Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders

“…The unobserved factors (i.e., hidden confounders), which typically happen at irregular time stamps and are not reflected in finance system records or are difficult to observe, could bring bias by influencing both interventions and stock returns. The reason is that even a small number of existing factors (such as Small Minus Big and High Minus Low) could significantly explain the crosssection of stock returns (D'Acunto et al 2021). If we can simulate such hidden confounders within a reasonable range, we are able to obtain treatment estimates with reduced bias and variance by making appropriate impact assumptions on the relationship between treatments and outcomes (Wang and Blei 2019).…”