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

TL;DR

This paper enhances regression-based event study analysis by integrating a topological machine-learning approach using self-organizing maps, enabling better identification of abnormal return factors and event clustering, especially in biased, event-clustered markets.

Contribution

It introduces a novel correction scheme employing self-organizing maps to improve the accuracy of event studies in market analysis.

Findings

Factors of abnormal stock returns are more easily identified.

Event clusters can be effectively depicted.

The new method reduces bias in event-clustered market situations.

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 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 event-clustered 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.