Abstract
We provide a common framework that relates traditional event study estimation methods in finance with a modern approach for causal event studies. This framework is called synthetic portfolio and is a particular case of synthetic control methods. We provide a simulation exercise and an empirical application to evaluate the performance of the method. In addition, synthetic control methods provides a reliable framework, for test based on the abnormal returns, that overcomes some difficulties in the traditional test. We conclude that the market model provides a counterfactual as good as a synthetic control.
Highlights
Event studies is one of the most widely used methodologies in accounting and financial research ([1]), and in certain legal proceedings
The claim is that better research designs and statistical methods, in empirical microeconomics, have increased the credibility of the implications obtained in these studies. [4] provide an analysis of the use and potential of causal inference methods in the field of accounting research
Their main conclusions are as follows: accounting research does primarily address problems that are causal in nature; there is an increase in the use of quasi-experimental methods in addressing causal questions in accounting research; there is still a lot to be done in the field to use the tools that are already available and have been successfully used in empirical microeconomics; the authors emphasize the use of causal diagrams and structural models in accounting research
Summary
Event studies is one of the most widely used methodologies in accounting and financial research ([1]), and in certain legal proceedings. The notion of a synthetic portfolio provided a common framework that relates traditional event study techniques, in particular the use of the market model, and synthetic control method, which is has been gaining popularity in the causal event literature in recent years. We provide simulation results to evaluate the performance of the different method and provide an empirical application using as event merger announcements In addition this new methodological framework that we claim encompasses traditional approaches provides additional and valuable insights to perform statistical inference over the individual abnormal returns (the treatment effects) that was hardly possible with the traditional testing framework.
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