[1H3-OS-8a] OS-8

[1H3-OS-8a-05] Revealing Hidden Alpha in Large-Cap Stocks

LLM-Driven Sentiment Analysis of Japanese 10-K Reports

〇Moe Nakasuji¹, Katsuhiko Okada¹, Yasutomo Tsukioka¹, Takahiro Yamasaki² (1. Kwansei Gakuin University, 2. Osaka Sangyo University)

Keywords:LLM, return predictability, 10-K report, MD&A

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.