Modeling News Interactions and Influence for Financial Market Prediction (2024)

Abstract: 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.

## introduction media coverage and news events have been shown to influence market returns @xcite . however, the process through which news diffuses into market prices is complex. the influence of news items on market outcomes varies, affected by various market conditions @xcite . since existing studies often adopt a simplified approach by treating available news data holistically and investigating its overall effect on the market @xcite , the nuanced information contained within individual news items is overlooked. consequently, parsing the market information diffusion process in detail by considering the impact of each news item emerges as a potential approach to enhance market prediction. figure 1 : news importance fluctuates when considered in the context of other news. breaking news (news 2) may diminish the market impact of another news item (news 1). however, if related breaking news (news 3) is considered, the influence of news 1 could resurge. however, evaluating the influence of individual news items is challenging. news does not exist in isolation but is connected with other information. studies indicate that information diffusion within the market is a gradual process (albert jr and @xcite . financial news exhibits a "long memory effect", a persistent impact over time @xcite . during this dissemination process, the influence of an individual news item can be further modulated by interactions with other news items, as illustrated by the practical example in figure 1 . given the sheer volume of news, evaluating the market impact of news items becomes even more challenging. this paper aims to address this gap. the domain of financial news presents an excellent testbed for not only improving market prediction but also enhancing our understanding of how to effectively leverage multi-modal data. modeling news interactions requires a more comprehensive use and exploration of multi-modal data in the form of both numerical and textual data from market and news sources. it also requires considering established market theories (see §2), such as the efficient market hypothesis and information diffusion theory. we develop finin, a financial interconnected news influence network to build interactions between news reports and analyze their diverse impact on markets. the model comprises two key components: a data fusion encoder and a marketaware influence quantifier. the data fusion encoder handles multi-modal data by encoding various inputs differently while maintaining consistency for data within the same domain. it encodes information related to each news report as independent features, providing a foundation for modeling complex information interactions. the market-aware influence quantifier draws upon financial theories to analyze relationships both between news items themselves and between news and prices. it evaluates the market influence of each news item by considering the overall market context. experiments on two major markets, s&p 500 and nasdaq 100, demonstrate the superiority of finin over state-of-the-art methods. using a gpt-based method @xcite as a baseline, we show that large language models (llms) cannot be directly used for managing large volumes of news for market prediction. while llms perform well on financial sentiment analysis, finin demonstrates a better ability to build relationships between news and market movements. additionally, our results provide three key insights into the market information diffusion process. firstly, finin highlights a delay in the market pricing of news, indicating market inefficiency in incorporating news information. secondly, we corroborate the long memory effect of news @xcite . thirdly, we identify the limitations of financial news sentiment analysis and describe how finin overcomes these limitations. these findings enhance our understanding of the market's response to news. to summarize, our main contributions are: ## method we have designed the finin model to evaluate the impact of news on price changes. the finin model comprises two primary components: a data fusion encoder and a market-aware influence quantifier. by leveraging both textual and numerical data, finin can capture the mutual influence between these disparate pieces of information. figure 2 illustrates an overview of the finin model. ## conclusions this paper introduces finin, a novel model designed to use financial news for market prediction. finin effectively uses market-related information from news data, outperforming various advanced market prediction methods. our experiments shed light on the relationships between news and the market, highlighting delayed market pricing of news, the long memory effect of financial news, and the limitations of market sentiment analysis in extracting predictive power from news.