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Esg Investing: A Sentiment Analysis Approach

Author

Listed:
  • Stéphane Goutte

    (Université Paris-Saclay)

  • Viet Hoang Le

    (Université Paris-Saclay)

  • Fei Liu

    (IPAG Business School)

  • Hans-Jörg Mettenheim, Von

    (IPAG Business School)

Abstract

We analyze the predictability of news sentiment (both general news and ESG-related news) on the return of stocks from European and the potential of applying them as a proper trading strategy over seven years from 2015 to 2022. We find that sentiment indicators extracted from news supplied by GDELT such as Tone, Polarity, and Activity Density show significant relationships to the return of the stock price. Those relationships can be exploited, even in the most naive way, to create trading strategies that can be profitable and outperform the market. Furthermore, those indicators can be used as inputs for more sophisticated machine learning algorithms to create even better-performing trading strategies. Among the indicators, those extracted from ESG-related news tend to show better performance in both cases: when they are used naively or as inputs for machine learning algorithms.

Suggested Citation

  • Stéphane Goutte & Viet Hoang Le & Fei Liu & Hans-Jörg Mettenheim, Von, 2023. "Esg Investing: A Sentiment Analysis Approach," Working Papers halshs-03917335, HAL.
    • Handle: RePEc:hal:wpaper:halshs-03917335
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03917335
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    References listed on IDEAS

    as
    1. Gunther Capelle-Blancard & Aurélien Petit, 2019. "Every Little Helps? ESG News and Stock Market Reaction," Journal of Business Ethics, Springer, vol. 157(2), pages 543-565, June.
    2. Paul C. Tetlock, 2014. "Information Transmission in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 6(1), pages 365-384, December.
    3. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Stephen J. Terry, 2020. "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc.
    4. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    5. Fraiberger, Samuel P. & Lee, Do & Puy, Damien & Ranciere, Romain, 2021. "Media sentiment and international asset prices," Journal of International Economics, Elsevier, vol. 133(C).
    6. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    7. Kvam, Emilie & Molnar, Peter & Wankel, Ingvild & Odegaard, Bernt Arne, 2022. "Do sustainable company stock prices increase with ESG scrutiny? Evidence using social media," UiS Working Papers in Economics and Finance 2022/1, University of Stavanger.
    8. Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019. "Predicting Returns With Text Data," NBER Working Papers 26186, National Bureau of Economic Research, Inc.
    9. Eugene F. Fama & Kenneth R. French, 2004. "The Capital Asset Pricing Model: Theory and Evidence," Journal of Economic Perspectives, American Economic Association, vol. 18(3), pages 25-46, Summer.
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    More about this item

    Keywords

    ESG Stock Market Prediction Sentiment Analysis Machine Learning Big Data GDELT; ESG; Stock Market Prediction; Sentiment Analysis; Machine Learning; Big Data; GDELT;
    All these keywords.

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