Causal Inference in Financial Event Studies

2025-11-19

AI Summary

In financial event studies, misspecified linear factor models yield inconsistent abnormal return estimates—particularly under non-random event timing, extended event windows, or high-volatility market conditions. This paper identifies the root cause of this inconsistency and proposes a synthetic control approach that dispenses with stringent factor structure assumptions. By constructing a replicating portfolio from control securities to estimate counterfactual returns—and integrating asymptotic bias analysis with formal identification conditions—it replaces conventional factor-based modeling with a more robust quasi-experimental framework. Four empirical applications demonstrate that several canonical findings may stem from model misspecification; the proposed method substantially improves the reliability and accuracy of causal effect estimation, especially over longer horizons and during periods of elevated market volatility.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Traditional event study estimators produce inconsistent treatment effects under model misspecification
• Bias is severe during volatile periods and when event timing correlates with markets
• Established empirical findings may reflect model misspecification rather than true effects

Innovation

• Synthetic control methods replace linear factor models
• Construct replicating portfolios without factor structures
• Address misspecification bias in financial event studies