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Impact of Sentiment analysis on Energy Sector Stock Prices : A FinBERT Approach

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  • Sarra Ben Yahia

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Jose Angel Garcia Sanchez

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Rania Hentati Kaffel

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

This study provides sentiment analysis model to enhance market return forecasts by considering investor sentiment from social media platforms like Twitter (X). We leverage advanced NLP techniques and large language models to analyze sentiment from financial tweets. We use a large web-scrapped data of selected energy stock daily returns spanning from 2018 to 2023. Sentiment scores derived from FinBERT are integrated into a novel predictive model (SIMDM) to evaluate autocorrelation structures within both the sentiment scores and stock returns data. Our findings reveal i) significant correlations between sentiment scores and stock prices. ii) Results are highly sensitive to data quality. iii) Our study reinforces the concept of market efficiency and offers empirical evidence regarding the delayed influence of emotional states on stock returns.

Suggested Citation

  • Sarra Ben Yahia & Jose Angel Garcia Sanchez & Rania Hentati Kaffel, 2024. "Impact of Sentiment analysis on Energy Sector Stock Prices : A FinBERT Approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-04629569, HAL.
    • Handle: RePEc:hal:cesptp:hal-04629569
    Note: View the original document on HAL open archive server: https://paris1.hal.science/hal-04629569
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    Keywords

    financial NLP finBERT information extraction webscraping sentiment analysis; financial NLP; finBERT; information extraction; webscraping; sentiment analysis; LLM; Deep learing;
    All these keywords.

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