Predicting stock price trends using language models to extract the sentiment from analyst reports: Evidence from IBEX 35-listed companies

Alejandro Moreno and Joaquín Ordieres-Meré

Economics Letters, 2025, vol. 254, issue C

Abstract: This study investigates the utility of large language models to extract sentiment from sell-side equity analysts’ reports and their potential ability to predict stock price trends, using the IBEX 35 index as a case study. The RoBERTa, FinBERT, and GPT natural language processing models are employed to analyze a corpus of analysts’ equity research reports over 2016–2022. The results indicate that the extracted sentiment can serve as a valuable tool for forecasting stock price movements, avoiding the potential bias in analyst reports when assigning a target price. This highlights the transformative potential of language models in the financial industry and their role in assisting investors in making informed investment decisions.

Keywords: Natural language processing; Large language models; Stock market prediction; Analyst recommendations (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:254:y:2025:i:c:s0165176525002411

DOI: 10.1016/j.econlet.2025.112404

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