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arXiv · 1905.06536

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

Takashi Yamashita·Ryozo Miura
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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.

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Takashi Yamashita, Ryozo Miura. 2019-05-16. Improving Regression-based Event Study Analysis Using a Topological Machine-learning Method. https://arxiv.org/abs/1905.06536

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