Paul Goldsmith-Pinkham, Tianshu Lyu
arXiv 19 Nov 2025 · Econometrics
arXiv:2511.15123 · PDF · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Acemoglu, Daron and Johnson, Simon and Kermani, Amir and Kwak, James… (2016) The value of connections in turbulent times: Evidence from the United States | 0.971 | 12 | 4 | 92% |
| 2 | Xu, Yiqing (2017) Generalized synthetic control method: Causal inference with interactive fixed effects models | 0.894 | 7 | 4 | 71% |
| 3 | Campbell, J.Y. and Lo, A.W. and MacKinlay, A.C (1997) The Econometrics of Financial Markets | 0.874 | 5 | 2 | 100% |
| 4 | Kelly, Bryan T and Pruitt, Seth and Su, Yinan (2019) Characteristics are covariances: A unified model of risk and return | 0.874 | 5 | 2 | 100% |
| 5 | Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens (2010) Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program | 0.843 | 3 | 3 | 100% |
| 6 | Barber, Brad M and Lyon, John D (1997) Detecting long-run abnormal stock returns: The empirical power and specification of test statistics | 0.811 | 4 | 2 | 100% |
| 7 | Malmendier, Ulrike and Moretti, Enrico and Peters, Florian S (2018) Winning by losing: Evidence on the long-run effects of mergers | 0.811 | 4 | 2 | 100% |
| 8 | Ferman, Bruno (2021) On the properties of the synthetic control estimator with many periods and many controls | 0.737 | 5 | 2 | 60% |
| 9 | Savor, Pavel G and Lu, Qi (2009) Do stock mergers create value for acquirers? | 0.737 | 3 | 2 | 100% |
| 10 | Shleifer, Andrei (1986) Do demand curves for stocks slope down? | 0.737 | 3 | 2 | 100% |
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