Causal Inference in Financial Event Studies

TL;DR

This paper highlights the limitations of traditional linear factor models in financial event studies under misspecification, especially during volatile periods, and proposes synthetic control methods as a more reliable alternative.

Contribution

It introduces synthetic control methods for causal inference in financial event studies, addressing biases from model misspecification and providing guidance on method reliability.

Findings

Traditional models can be biased during volatile periods.

Synthetic control methods improve causal inference accuracy.

Some established findings may be due to model misspecification.

Abstract

Financial event studies, ubiquitous in finance research, typically use linear factor models with known factors to estimate abnormal returns and identify causal effects of information events. This paper demonstrates that when factor models are misspecified -- an almost certain reality -- traditional event study estimators produce inconsistent estimates of treatment effects. The bias is particularly severe during volatile periods, over long horizons, and when event timing correlates with market conditions. We derive precise conditions for identification and expressions for asymptotic bias. As an alternative, we propose synthetic control methods that construct replicating portfolios from control securities without imposing specific factor structures. Revisiting four empirical applications, we show that some established findings may reflect model misspecification rather than true treatment effects. While traditional methods remain reliable for short-horizon studies with random event timing, our results suggest caution when interpreting long-horizon or volatile-period event studies and highlight the importance of quasi-experimental designs when available.