@inproceedings{e03ee31758544eb5ad491eec24c01063,
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.",
keywords = "computational finance, sparse optimization, text mining",
author = "Felix Ming and Fai Wong and Zhenming Liu and Mung Chiang",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ICDM.2014.116",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "January",
pages = "430--439",
editor = "Ravi Kumar and Hannu Toivonen and Jian Pei and {Zhexue Huang}, Joshua and Xindong Wu",
booktitle = "Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014",
address = "United States",
edition = "January",
note = "14th IEEE International Conference on Data Mining, ICDM 2014 ; Conference date: 14-12-2014 Through 17-12-2014",
}