IDEAS home Printed from https://ideas.repec.org/p/cfi/fseres/cf601.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.carf.e.u-tokyo.ac.jp/wp/wp-content/uploads/2025/03/F601.pdf
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhenwei Lin & Masafumi Nakano & Akihiko Takahashi, 2025. "Investment with New Sentiment Analysis in Japanese Stock Market: Expert Knowledge Can Still Outperform ChatGPT," CIRJE F-Series CIRJE-F-1248, CIRJE, Faculty of Economics, University of Tokyo.
    2. Daiya Mita & Akihiko Takahashi, 2022. "Multi-Agent Model Based Proactive Risk Management For Equity Investment," CIRJE F-Series CIRJE-F-1207, CIRJE, Faculty of Economics, University of Tokyo.
    3. Akihiko Takahashi & Soichiro Takahashi, 2022. "A state space modeling for proactive management in equity investment "Forthcoming in International Journal of Financial Engineering"," CARF F-Series CARF-F-543, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Akihiko Takahashi & Soichiro Takahashi, 2022. "A State Space Modeling for Proactive Management in Equity Investment," CIRJE F-Series CIRJE-F-1197, CIRJE, Faculty of Economics, University of Tokyo.
    5. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    6. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    7. Taiga Saito & Akihiko Takahashi, 2021. "Portfolio Optimization with Choice of a Probability Measure," CIRJE F-Series CIRJE-F-1165, CIRJE, Faculty of Economics, University of Tokyo.
    8. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2023. "Multi-agent Robust Optimal Investment Problem in Incomplete Market," CARF F-Series CARF-F-575, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    9. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2024. "Multi-agent Equilibrium Model with Heterogeneous Views on Fundamental Risks in Incomplete Market," CIRJE F-Series CIRJE-F-1224, CIRJE, Faculty of Economics, University of Tokyo.
    10. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2021. "Equilibrium Multi-Agent Model with Heterogeneous Views on Fundamental Risks," CIRJE F-Series CIRJE-F-1173, CIRJE, Faculty of Economics, University of Tokyo.
    11. Taiga Saito & Akihiko Takahashi, 2022. "Portfolio optimization with choice of a probability measure (forthcoming in proceedings of IEEE CIFEr 2022)," CARF F-Series CARF-F-534, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    12. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2022. "Multi-agent Robust Optimal Investment Problem in Incomplete Market," CIRJE F-Series CIRJE-F-1198, CIRJE, Faculty of Economics, University of Tokyo.
    13. Keisuke Kizaki & Taiga Saito & Akihiko Takahashi, 2024. "Multi-agent Equilibrium Model with Heterogeneous Views on Fundamental Risks in Incomplete Market," CARF F-Series CARF-F-578, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    14. Fang, Yi & Wang, Qi & Wang, Yanru & Yuan, Yan, 2024. "Media sentiment, deposit stability and bank systemic risk: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 1150-1172.
    15. Gric, Zuzana & Ehrenbergerova, Dominika & Hodula, Martin, 2022. "The power of sentiment: Irrational beliefs of households and consumer loan dynamics," Journal of Financial Stability, Elsevier, vol. 59(C).
    16. Oscar Calvo-Gonz'alez & Axel Eizmendi & Germ'an Reyes, 2022. "The Shifting Attention of Political Leaders: Evidence from Two Centuries of Presidential Speeches," Papers 2209.00540, arXiv.org, revised Jun 2023.
    17. Haiyuan Yin & Baifan Chen & Xiaoxiao Wang, 2022. "Supervisory capability of supervisor board, incentives to supervisor board, and stock price crash risk," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 622-649, April.
    18. Ali Asgarov, 2023. "Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing," Papers 2309.00136, arXiv.org.
    19. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    20. Goedde-Menke, Michael & Langer, Thomas & Pfingsten, Andreas, 2014. "Impact of the financial crisis on bank run risk – Danger of the days after," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 522-533.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cfi:fseres:cf601. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/catokjp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.