用多模态深度学习预测财报日股价方向，融合新闻情感与财务数据
Predicting Stock Price Direction on Earnings Announcement Days using Multi-modal Deep Learning
- 融合15个财务指标、3个技术指标和新闻情感，构建多模态特征空间
- Transformer模型在识别波动行情上表现更优，宏观F1得分更高
- 新闻情感信息显著提升预测效果，尤其适合量化交易研究者
由于市场噪声和高影响的价格断点，预测财报发布日的股价走势是一项重大挑战。本文评估了财报前新闻情绪、公司基本面及近期市场动态是否能共同预测股票在财报日的价格变动方向。我们构建了一个包含15个基本面指标、3个价格型技术指标以及通过FinBERT处理金融新闻文章得出的情绪分数的多模态特征空间。对比了LSTM网络与基于Transformer的架构与逻辑回归基线模型，并进一步评估了有无新闻情绪特征时各模型的表现，以量化其增量价值。结果表明，尽管LSTM通过保守策略实现更高精度，但Transformer模型在识别剧烈波动方面表现出更强敏感性，宏观F1得分更高；消融实验显示，引入新闻情绪信息始终带来稳定提升。
原文摘要 · Abstract (English)
Predicting stock price movements during Earnings Announcements (EAs) is a significant challenge due to market noise and high-impact price discontinuities. In this study, we evaluate whether pre-announcement news sentiment, firm fundamentals, and recent market dynamics jointly predict the directional price movement of equities on EA days. We construct a multi-modal feature space combining 15 fundamental metrics, 3 price-based technical indicators and sentiment scores derived from financial news articles processed using FinBERT. We compare a Long Short-Term Memory (LSTM) network and a Transformer-based architecture against a logistic regression baseline, and further assess all models with and without sentiment features to quantify their incremental value. Our results indicate that while the LSTM demonstrates higher precision through a conservative safe-bet strategy, the Transformer model exhibits superior sensitivity in identifying volatile movements, achieving a higher macro F1-score, with ablation experiments showing a consistent benefit from incorporating news sentiment.
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