Improving Regression-based Event Study Analysis Using a Topological Machine-learning Method
General Economics
2019-05-17 v1 Economics
Statistical Finance
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.
Cite
@article{arxiv.1905.06536,
title = {Improving Regression-based Event Study Analysis Using a Topological Machine-learning Method},
author = {Takashi Yamashita and Ryozo Miura},
journal= {arXiv preprint arXiv:1905.06536},
year = {2019}
}