建模新闻间互动与市场影响，提升金融预测精度
Modeling News Interactions and Influence for Financial Market Prediction
- 构建新闻互联网络，融合多模态数据捕捉新闻间交互
- 在标普500和纳斯达克100上分别提升0.429和0.341日度夏普比率
- 揭示新闻延迟定价、长期记忆效应及情感分析局限性
金融新闻向市场价格的扩散过程复杂，难以评估新闻事件与市场波动之间的关联。本文提出FININ（金融新闻互联影响网络）模型，不仅捕捉新闻与价格间的联系，还建模新闻之间的相互作用。该模型有效整合市场数据与新闻文本的多模态信息。我们在两个数据集上进行了广泛实验，涵盖15年期间的标普500和纳斯达克100指数，以及超过270万条新闻文章。结果表明，FININ显著优于现有先进市场预测模型，在两个市场的日度夏普比率分别提升了0.429和0.341。此外，研究揭示了新闻的延迟定价现象、长期记忆效应，以及金融情感分析在充分提取新闻预测能力方面的局限性。
原文摘要 · Abstract (English)
The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ's effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data.
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