English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper introduces a new correction scheme to a conventionalregression-based event study method: a topological machine-learning approachwith a self-organizing map (SOM).We use this new scheme to analyze a majormarket event in Japan and find that the factors of abnormal stock returns canbe easily can be easily identified and the event-cluster can be depicted.Wealso find that a conventional event study method involves an empirical analysismechanism that tends to derive bias due to its mechanism, typically in anevent-clustered market situation. We explain our new correction scheme andapply it to an event in the Japanese market --- the holding disclosure of theGovernment Pension Investment Fund (GPIF) on July 31, 2015.

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