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.

翻译：金融新闻向市场价格的扩散是一个复杂过程，这使得评估新闻事件与市场波动之间的关联变得极具挑战性。本文提出了一种新颖的市场预测模型FININ（金融互联新闻影响力网络），该模型不仅捕捉新闻与价格之间的联系，还捕捉新闻条目之间的交互作用。FININ有效地整合了来自市场数据和新闻报道的多模态信息。我们在两个数据集上进行了广泛的实验，涵盖了标普500指数和纳斯达克100指数超过15年的数据以及超过270万篇新闻文章。结果表明，FININ在两个市场的日夏普比率上分别实现了0.429和0.341的提升，其有效性超越了先进的市场预测模型。此外，我们的研究结果揭示了关于金融新闻的深刻见解，包括新闻的市场定价延迟、新闻的长记忆效应，以及金融情感分析在从新闻数据中充分提取预测能力方面的局限性。