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.

翻译：本文通过利用基于大语言模型（LLM）的新闻情感分析来研究股价走势预测问题。先前的研究大多分别侧重于提出和评估情感分析模型与股价走势预测方法。尽管已取得有前景的成果，但对于新闻情感在此任务中的益处，以及在此背景下对不同架构类型的全面评估，仍缺乏清晰而深入的理解。为此，我们开展了一项评估研究，比较了三种不同的大语言模型（即 DeBERTa、RoBERTa 和 FinBERT）在情感驱动的股票预测中的表现。我们的结果表明，DeBERTa 的表现优于其他两种模型，准确率达到 75%，而将三种模型组合的集成模型可将准确率提升至约 80%。此外，我们发现新闻情感特征能够（略微）提升某些股市预测模型的性能，即基于 LSTM、PatchTST 和 tPatchGNN 的分类器，以及基于 PatchTST 和 TimesNet 的回归任务模型。