Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization
This addresses stock market prediction for investors by improving accuracy and returns, though it is incremental as it builds on existing text mining and matrix factorization methods.
The paper tackles stock price prediction using Wall Street Journal articles by proposing a unified latent space model that captures correlations among stocks, among articles, and between them, achieving 55.7% accuracy and 56% return in backtesting.
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.