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1 janvier 2023
- handle: 10670/1.v19wmq
- halshs: halshs-03917335
info:eu-repo/semantics/OpenAccess
Mots-clés
ESG Stock Market Prediction Sentiment Analysis Machine Learning Big Data GDELT ESG Stock Market Prediction Sentiment Analysis Machine Learning Big Data GDELT -fin] -fin]/Computational Finance [q-fin.CP] -fin]/Pricing of Securities [q-fin.PR] -fin]/Statistical Finance [q-fin.ST]Sujets proches
Funds Funding Finance, commerce, confiscations, etcCiter ce document
Stéphane Goutte et al., « ESG INVESTING: A SENTIMENT ANALYSIS APPROACH », HAL-SHS : économie et finance, ID : 10670/1.v19wmq
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Résumé
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.