Does the market model provide a good counterfactual for event studies in finance?
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We provide a common framework that relates traditional event study estimation methods in finance to a modern approach for causal event studies. The framework provides a model for abnormal returns that nests the fitted market model (the traditional approach) and more recent approaches based on difference-in-differences and synthetic control methods. We show that a synthetic control method in this context can be understood as a synthetic portfolio. We provide a simulation exercise and an empirical application, using mergers and acquisitions as the event of interest, to evaluate the performance of the different models within the framework. Our results indicate that causal inference methods such as synthetic matching or difference-in-differences do not provide an improvement over the traditional approach based on the fitted market model. Although the fitted market model may not always abide by the conditions under which it is considered a proper counterfactual, its performance indicates that it is still a good potential outcome.
KeywordsEvent study Synthetic control method Portfolio optimization Merger announcement
JEL ClassificationG11 C13 G34
This research was completed while the author was visiting the Finance Department of the University of Connecticut School of Business. The author would like to thank Chinmoy Ghosh for the invitation. The author would also like to thank Carlos Pombo and Ivan Montoya for providing the data on mergers in Colombia. This paper benefited from the comments received from the anonymous referees, Jose Martinez, Cristian Pinto, and seminar participants at the 12th International Conference on Computational and Financial Econometrics, Universidad EAFIT, the business school and the actuarial science group at the Department of Mathematics at the University of Connecticut. We thank Maxine Garcia, Ph.D., from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.
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