Paper Info
Title
Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization
Abstract
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 back testing 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.
Year
DOI
Venue
2014
10.1109/ICDM.2014.116
ICDM '14 Proceedings of the 2014 IEEE International Conference on Data Mining
Keywords
Field
DocType
data mining,electronic trading,matrix decomposition,sparse matrices,stock markets,text analysis,Sharpe ratio,The Wall Street Journal,WSJ,directional movement,news article,sparse matrix factorization,stock market prediction,stock price,text mining,trading strategy,unified latent space model,computational finance,sparse optimization,text mining
Trading strategy,Data mining,Sparse matrix factorization,Leverage (finance),Computational finance,Computer science,Prediction algorithms,Sharpe ratio,Artificial intelligence,Stock (geology),Stock market prediction,Machine learning
Conference
Volume
ISSN
Citations 
abs/1406.7330
1550-4786
8
PageRank 
References 
Authors
0.47
12
4
Name
Order
Citations
PageRank
Felix Ming180.47
Fai Wong2358.87
Zhenming Liu341925.35
Mung Chiang47303486.32