融合新闻情感与市场数据，提升股票走势预测准确率
Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
- 用跨模态注意力融合文本情感嵌入与数值指标
- 回测显示优于纯数值基线模型
- 提供可扩展的多模态金融预测方法指南
我们提出 STONK（基于新闻知识的股票优化），一种整合数值市场指标与情感增强新闻嵌入的多模态框架，以改善每日股票走势预测。通过特征拼接与跨模态注意力机制联合数值与文本嵌入，统一管道克服了单一分析的局限性。回测结果表明，STONK 显著优于仅使用数值特征的基线模型。对多种融合策略与模型配置的综合评估，为可扩展的多模态金融预测提供了实证指导。源代码已开源于 GitHub。
原文摘要 · Abstract (English)
We propose STONK (Stock Optimization using News Knowledge), a multimodal framework integrating numerical market indicators with sentiment-enriched news embeddings to improve daily stock-movement prediction. By combining numerical & textual embeddings via feature concatenation and cross-modal attention, our unified pipeline addresses limitations of isolated analyses. Backtesting shows STONK outperforms numeric-only baselines. A comprehensive evaluation of fusion strategies and model configurations offers evidence-based guidance for scalable multimodal financial forecasting. Source code is available on GitHub
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