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sentiment analysis, text mining, large language models, natural language processing, ChatGPT, Japanese stock market, TOPIX 500, Nikkei 225, investment, alpha creation, risk-adjusted returns

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  • Zhenwei Lin

    (Graduate School of Economics, University of Tokyo)

  • Masafumi Nakano

    (GCI Asset Management)

  • Akihiko Takahashi

    (Graduate School of Economics, The University of Tokyo)

Abstract

This paper presents a novel approach to sentiment analysis in the context of investments in the Japanese stock market. Specifically, we begin by creating an original set of keywords derived from news headlines sourced from a Japanese financial news platform. Subsequently, we develop new polarity scores for these keywords, based on market returns, to construct sentiment lexicons. These lexicons are then utilized to guide investment decisions regarding the stocks of companies included in either the TOPIX 500 or the Nikkei 225, which are Japan’s representative stock indices. Furthermore, empirical studies validate the effectiveness of our proposed method, which significantly outperforms a ChatGPT-based sentiment analysis approach. This provides strong evidence for the advantage of integrating market data into textual sentiment evaluation to enhance financial investment strategies.

Suggested Citation

  • Zhenwei Lin & Masafumi Nakano & Akihiko Takahashi, 2024. "sentiment analysis, text mining, large language models, natural language processing, ChatGPT, Japanese stock market, TOPIX 500, Nikkei 225, investment, alpha creation, risk-adjusted returns," CARF F-Series CARF-F-601, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, revised Apr 2025.
    • Handle: RePEc:cfi:fseres:cf601
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