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From Tweets to Returns: Validating LLM-Based Sentiment Signals in Energy Stocks

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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

Our research assesses the predictive value of LLM-based sentiment in forecasting energy stock returns. Using FinBERT-derived sentiment indicators from 415,193 tweets spanning 2018-2024, we find statistically significant causal relationships for 80% of companies analyzed. Our VAR analysis reveals heterogeneous optimal lag structures ranging from 2 to 14 days, providing econometric evidence against semi-strong market efficiency. Our results show that the accuracy of the forecast depends critically on the quality and coverage of the data. Our contribution is twofold: (i) a scalable LLMdriven pipeline to quantify firm-level sentiment at daily frequency, and (ii) an econometric validation via VAR/Granger that uncovers economically meaningful lead-lag patterns

Suggested Citation

  • Sarra Ben Yahia & Jose Angel Garcia Sanchez & Rania Hentati Kaffel, 2025. "From Tweets to Returns: Validating LLM-Based Sentiment Signals in Energy Stocks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-05312326, HAL.
    • Handle: RePEc:hal:cesptp:hal-05312326
    Note: View the original document on HAL open archive server: https://paris1.hal.science/hal-05312326v1
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