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

Sarra Ben Yahia (), Jose Angel Garcia Sanchez () and Rania Kaffel ()
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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 Kaffel: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

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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

Keywords: sentiment analysis; LLM; FinBERT; energy equity markets; Twitter/X sentiment; return forecasting; webscraping; information diffusion; information extraction; finBERT; financial NLP; VAR (search for similar items in EconPapers)
Date: 2025-09-30
Note: View the original document on HAL open archive server: https://paris1.hal.science/hal-05312326v1
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