2019/05/16 by Takashi Yamashita, Yamashita, Takashi, Ryozo Miura +1
Economics, Econometrics and Finance · #FOS: Economics and business #General Economics (econ.GN) #Statistical Finance (q-fin.ST) #econ.GN #q-fin.EC #q-fin.ST
paper · pdf · doi:10.48550/arxiv.1905.06536
arxiv created 2019/05/16 · arxiv updated 2019/05/17
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.