ESG Investing: A Sentiment Analysis Approach - Université de Versailles Saint-Quentin-en-Yvelines Access content directly
Journal Articles SSRN Electronic Journal Year : 2023

ESG Investing: A Sentiment Analysis Approach

Fei Liu
  • Function : Author
Hoang Viet Le
  • Function : Author
Hans-Jorg Von Mettenheim
  • Function : Author

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.
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Dates and versions

hal-04474905 , version 1 (23-02-2024)

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Stephane Goutte, Fei Liu, Hoang Viet Le, Hans-Jorg Von Mettenheim. ESG Investing: A Sentiment Analysis Approach. SSRN Electronic Journal, 2023, ⟨10.2139/ssrn.4316107⟩. ⟨hal-04474905⟩
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