# Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization

arXiv: https://arxiv.org/abs/1406.7330 · HTML: https://mlsanity.com/paper/1406.7330/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2014.

## Built on by (most cited first, 4 of 4)

- [Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks](https://mlsanity.com/paper/2201.04965/) (2022)
- [Detection of financial opportunities in micro-blogging data with a stacked classification system](https://mlsanity.com/paper/2404.07224/) (2024)
- [Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models](https://mlsanity.com/paper/2212.00852/) (2022)
- [Permutation-Invariant Subgraph Discovery](https://mlsanity.com/paper/2104.01063/) (2021)

## Abstract (arXiv)

We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and news articles. Unlike many existing approaches, our new model is able to simultaneously leverage the correlations: (a) among stock prices, (b) among news articles, and (c) between stock prices and news articles. Thus, our model is able to make daily predictions on more than 500 stocks (most of which are not even mentioned in any news article) while having low complexity. We carry out extensive backtesting on trading strategies based on our algorithm. The result shows that our model has substantially better accuracy rate (55.7%) compared to many widely used algorithms. The return (56%) and Sharpe ratio due to a trading strategy based on our model are also much higher than baseline indices.