[4J2-GS-6e] 言語メディア処理：自然言語処理(2/2)

[4J2-GS-6e-05] Investment Strategy utilizing the propagation of financial statements information with Economic Causal Chain and SSESTM model

〇Shingo Sashida¹, Kei Nakagawa¹ (1. Nomura Asset Management Co.,Ltd.)

Keywords:Economic Causal Chain, Stock Return Predictability, Lead Lag Effect, Sentiment Analysis, Natural Language Processing

In recent years, various text mining techniques have been utilized in the field of both academic and practical finance. The economic causal chain is one example and refers to a cause and effect network structure constructed by extracting a description indicating a causal relationship from the texts of financial statement summaries. There is the lead-lag effect which spreads to the ’effect ’stock group when a large stock fluctuation in the ’cause ’ stock group in the causal chain occurs. However, in economic causality among companies, a company’s positive effect can either positively or negatively affect another causally related companies. That is, considering positive or negative sentiments is important for considering the lead-lag effect in the economic causal chain. The SSESTM (Supervised Sentiment Extraction via Screening and Topic Modeling) model has been proposed as a sentiment analysis specialized for stock return forecasting, and it produced a substantial profit in the U.S. stock market. In this study, we propose an investment strategy that exploits the lead-lag effect in the causal chain relationship considering the sentiments with the SSESTM model. We confirm the profitability of our proposed strategy and there is the evidence of stock return predictability across causally linked companies considering sentiment.