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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

Author

Listed:
  • 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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    References listed on IDEAS

    as
    1. Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2020. "Interest Rate Model with Investor Attitude and Text Mining," CIRJE F-Series CIRJE-F-1152, CIRJE, Faculty of Economics, University of Tokyo.
    2. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-430, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    3. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-441, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    5. Huck, Nicolas, 2010. "Pairs trading and outranking: The multi-step-ahead forecasting case," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1702-1716, December.
    6. Masafumi Nakano & Akihiko Takahashi, 2020. "A new investment method with AutoEncoder: Applications to crypto currencies(Forthcoming in "Expert Systems with Applications")," CARF F-Series CARF-F-489, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    7. Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2020. "Interest Rate Model with Investor Attitude and Text Mining (Published in IEEE Access)," CARF F-Series CARF-F-479, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
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