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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
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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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