# Improving Stock Market Prediction via Heterogeneous Information Fusion

arXiv: https://arxiv.org/abs/1801.00588 · HTML: https://mlsanity.com/paper/1801.00588/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2018.

## Built on by (most cited first, 12 of 12)

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- [News-based Business Sentiment and its Properties as an Economic Index](https://mlsanity.com/paper/2110.10340/) (2021)
- [Measuring Consistency in Text-based Financial Forecasting Models](https://mlsanity.com/paper/2305.08524/) (2023)
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- [DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting](https://mlsanity.com/paper/2405.00522/) (2024)
- [Stock Market Analysis with Text Data: A Review](https://mlsanity.com/paper/2106.12985/) (2021)
- [Stock Market Prediction using Natural Language Processing -- A Survey](https://mlsanity.com/paper/2208.13564/) (2022)
- [Real-Time Online Stock Forecasting Utilizing Integrated Quantitative and Qualitative Analysis](https://mlsanity.com/paper/2311.15218/) (2023)
- [Implementation of a Type-2 Fuzzy Logic Based Prediction System for the Nigerian Stock Exchange](https://mlsanity.com/paper/2202.02107/) (2022)

## Abstract (arXiv)

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.