Improving Regression-based Event Study Analysis Using a Topological Machine-learning Method

Abstract

This paper introduces a new correction scheme to a conventional regression-based event study method: a topological machine-learning approach with a self-organizing map (SOM). We use this new scheme to analyze a major market event in Japan and find that the factors of abnormal stock returns can be easily identified and the event-cluster can be depicted. We also find that a conventional event study method involves an empirical analysis mechanism that tends to derive bias due to its mechanism, typically in an eventclustered market situation. We explain our new correction scheme and apply it to an event in the Japanese market -the holding disclosure of the Government Pension Investment Fund (GPIF) on July 31, 2015.

Investor’s behaviour to COVID-19 vaccine: an event study on health and pharmaceutical sector in India

“…The purpose of this academic article is to investigate investor behaviour in relation to the first dose of the COVID-19 vaccine using an event study methodology. The event study methodology is a popular approach in finance research (Yamashita and Miura, 2019) that examines the impact of particular activities on financial markets, such as the launch of a fresh offering or the publication of unexpected news (Lodha and Soral, 2015;Fama, 1991). An event analysis examines the impact of a certain event on a company's value using financial market data.…”