Revealing Hidden Alpha in Large-Cap Stocks

DOI

Bibliographic Information

Other Title
  • LLM-Driven Sentiment Analysis of Japanese 10-K Reports
  • LLM-Driven Sentiment Analysis of Japanese 10-K Reports
Published
2025
DOI
  • 10.11517/pjsai.jsai2025.0_1h3os8a05
Publisher
The Japanese Society for Artificial Intelligence

Description

<p>This study extends prior research on using large language models (LLMs) to uncover return-predictive sentiment in Japanese 10-K reports by focusing on highly liquid stocks. Building on a dataset of Tokyo Stock Exchange-listed firms (2014–2023) and previously established methodologies, we narrow our scope to the TOPIX 100 and TOPIX 500—indices composed of the largest Japanese companies by market capitalization. Despite expectations that these well-followed and actively traded stocks should incorporate public information more efficiently, LLM-derived sentiment still predicts future returns, with larger abnormal returns (alpha) than when all listed stocks are included. These findings highlight the robustness of LLM-based approaches in detecting subtle signals within corporate disclosures and challenge the notion that highly liquid markets fully reflect available information. By highlighting the predictive power of LLM-extracted sentiment in large-cap portfolios, this study offers practical insights into how advanced natural language processing can enhance investment strategies, even in supposedly efficient segments of the equity market.</p>

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