Impact of LLMs news Sentiment Analysis on Stock Price Movement Prediction

• URL: http://arxiv.org/abs/2602.00086v2
• Date: Thu, 05 Feb 2026 00:41:51 GMT
• Title: Impact of LLMs news Sentiment Analysis on Stock Price Movement Prediction
• Authors: Walid Siala, Ahmed Khanfir, Mike Papadakis,
• Abstract summary: This paper addresses stock price movement prediction by leveraging LLM-based news sentiment analysis.<n>We compare 3 different LLMs, namely, DeBERTa, RoBERTa and FinBERT, for sentiment-driven stock prediction.<n>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%.
• Score: 5.317864735982288
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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