Impact of LLMs news Sentiment Analysis on Stock Price Movement Prediction

Walid Siala, Ahmed Khanfir and Mike Papadakis
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Walid Siala: SnT, University of Luxembourg, Luxembourg
Ahmed Khanfir: RIADI, ENSI, University of Manouba, Tunisia
Mike Papadakis: SnT, University of Luxembourg, Luxembourg

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Abstract: This paper addresses stock price movement prediction by leveraging LLM-based news sentiment analysis. Earlier works have largely focused on proposing and assessing sentiment analysis models and stock movement prediction methods, however, separately. Although promising results have been achieved, a clear and in-depth understanding of the benefit of the news sentiment to this task, as well as a comprehensive assessment of different architecture types in this context, is still lacking. Herein, we conduct an evaluation study that compares 3 different LLMs, namely, DeBERTa, RoBERTa and FinBERT, for sentiment-driven stock prediction. Our results suggest that DeBERTa outperforms the other two models with an accuracy of 75% and that an ensemble model that combines the three models can increase the accuracy to about 80%. Also, we see that sentiment news features can benefit (slightly) some stock market prediction models, i.e., LSTM-, PatchTST- and tPatchGNN-based classifiers and PatchTST- and TimesNet-based regression tasks models.

Date: 2026-01, Revised 2026-02
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