Improving Stock Market Prediction via Heterogeneous Information Fusion

TL;DR

This paper proposes a novel multi-source data fusion framework using tensor and matrix factorization to improve stock market prediction by jointly modeling events, sentiments, and stock correlations, demonstrating effectiveness on Chinese and Hong Kong stock data.

Contribution

It introduces a coupled tensor and matrix factorization approach to integrate heterogeneous Web news, social media sentiments, and stock correlations for enhanced prediction accuracy.

Findings

Improved prediction accuracy on China A-share and HK stock data.

Effective modeling of multi-source heterogeneous data.

Joint prediction of correlated stocks outperforms independent methods.

Abstract

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people's sentiments towards the market and stocks, have been proved to play important roles in the stocks' volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users' sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsic relations among the events and the investors' sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model.