用大模型分析新闻情绪，提升股票走势预测准确率。
Impact of LLMs news Sentiment Analysis on Stock Price Movement Prediction
- 对比DeBERTa、RoBERTa、FinBERT三类大模型的情绪分析能力。
- DeBERTa准确率达75%，融合三模型可提升至80%。
- 新闻情绪特征对多种预测模型有轻微增益，适合金融量化研究者。
本文通过基于大语言模型（LLM）的新闻情感分析来解决股票价格走势预测问题。以往研究多分别关注情感分析模型或股价预测方法，缺乏对新闻情感在该任务中实际价值的深入理解，也缺少对不同架构类型的全面评估。为此，我们系统比较了DeBERTa、RoBERTa和FinBERT三种模型在情感驱动的股票预测中的表现。结果表明，DeBERTa表现最优，准确率达75%；而将三模型集成后，准确率可提升至约80%。此外，新闻情感特征对部分模型有一定辅助作用，包括基于LSTM、PatchTST和tPatchGNN的分类模型，以及基于PatchTST和TimesNet的回归模型。
原文摘要 · Abstract (English)
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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