Discovery of Rare Causal Knowledge from Financial Statement Summaries

TL;DR

This paper introduces a method to extract rare causal relationships from Japanese financial statement summaries by combining machine learning, syntactic pattern analysis, and rarity filtering.

Contribution

It presents a novel three-step approach for identifying rare causal knowledge specifically from financial summaries, enhancing understanding of less obvious cause-effect relationships.

Findings

Successfully extracted rare causal knowledge from financial summaries.

Demonstrated effectiveness of combining machine learning with syntactic pattern analysis.

Identified previously unknown causal relationships in financial data.

Abstract

What would happen if temperatures were subdued and result in a cool summer? One can easily imagine that air conditioner, ice cream or beer sales would be suppressed as a result of this. Less obvious is that agricultural shipments might be delayed, or that sound proofing material sales might decrease. The ability to extract such causal knowledge is important, but it is also important to distinguish between cause-effect pairs that are known and those that are likely to be unknown, or rare. Therefore, in this paper, we propose a method for extracting rare causal knowledge from Japanese financial statement summaries produced by companies. Our method consists of three steps. First, it extracts sentences that include causal knowledge from the summaries using a machine learning method based on an extended language ontology. Second, it obtains causal knowledge from the extracted sentences using syntactic patterns. Finally, it extracts the rarest causal knowledge from the knowledge it has obtained.