Event Study Methodology in Financial Research: Classical Foundations, Modern Developments and Future Directions

Abstract

One of the most commonly used empirical methodologies in financial economics is the event study methodology, which is used to examine the response of financial markets to new information. The main aim of this study is to review the development, theoretical foundation, and recent advancements in the analysis of financial events, along with their applications and possible areas for future research. To begin with, this study examines the theoretical foundation of the event study methodology, particularly in relation to the Efficient Market Hypothesis, which posits that stock prices reflect new information quickly. Next, this study presents the classical framework of the event study methodology, which includes the identification of events, estimation, and event windows, the use of expected return models, and abnormal return calculations. The conventional expected return models like the mean adjusted return model, market model, market adjusted model, and capital asset pricing model-based model are discussed, and conventional statistical tests like parametric and non-parametric statistical tests are discussed. The study has also discussed the conventional limitations of event study methods like event clustering, cross-sectional dependence, volatility changes, thin trading, and specification errors. In order to overcome the limitations of conventional event study methods, recent advancements in event study methods like the multifactor asset pricing models, GARCH-based models for volatility, the bootstrap approach, high-frequency event studies using intraday data, and long-horizon performance evaluation methods are discussed in the study. Additionally, the emerging trends in event study research, like machine learning, natural language processing, and big data analysis, are discussed in the study as the possible future directions of event study research.