Modeling News Interactions and Influence for Financial Market Prediction

TL;DR

This paper presents FININ, a novel model that captures news interactions and influences to improve financial market prediction, demonstrating significant performance gains and providing insights into news-market dynamics.

Contribution

Introduces FININ, a new model that integrates news interactions and influences for more accurate market prediction, surpassing existing models in performance.

Findings

FININ outperforms existing models in Sharpe ratio improvements.

Market pricing of news exhibits delays and long memory effects.

Financial sentiment analysis has limitations in predictive power.

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.