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

• Tác giả: Ryozo Miura, Takashi Yamashita
• Ngôn ngữ: eng
• Ký hiệu phân loại: 006.31 Machine learning
• Thông tin xuất bản: 2019
• Bộ sưu tập: Metadata
• ID: 162909

Tóm tắt

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.