[4K3-IS-2f] Machine learning

[4K3-IS-2f-01] Unraveling Global-to-Domain Events Influence based on Causal Graph

〇Ziwei Xu¹, Ryutaro Ichise^2,1 (1. National Institute of Advanced Industrial Science and Technology, 2. Institute of Science Tokyo)

Keywords:Finance, Causal graph, Event acquisition, Geographic mapping, Event ontology

Global events can significantly impact various sectors but detail the impacts from global events to a specific business remains complex. If these impacts could be represented explicitly, it will enhance event prediction across various domains and help to avoid risk. This paper addresses this challenge within the finance sector by proposing a method that uses causal graphs to link global events with company-specific business events through geographical and taxonomic mappings. We outline a framework encompassing event acquisition, geographic mapping, and event taxonomic mapping, and illustrate its application with an example, demonstrating how geographical and ontological factors reveal hidden influences on business events.