2013-02-21 (木) 18:38:47 (2842d) | Topic path: Top / GidofalviElkan01


Using News Articles to Predict Stock Price Movements Győző Gid�falvi Department of Computer Science and Engineering University of California, San Diego La Jolla, CA 92037 2001, June 15, 2001 Abstract This paper shows that short-term stock price movements can be predicted using financial news articles. Given a stock price time series, for each time interval we classify price movement as "up," "down," or (approximately) "unchanged" relative to the volatility of the stock and the change in a relevant index. Each article in a training set of news articles is then labeled "up," "down," or "unchanged" according to the movement of the associated stock in a time interval surrounding publication of the article. A na�ve Bayesian text classifier is trained to predict which movement class an article belongs to. Given a test article, the trained classifier potentially predicts the price movement of the associated stock. However, the efficient markets hypothesis asserts that this classifier cannot have predictive power. In careful experiments we find definite predictive power for the stock price movement in the interval starting 20 minutes before and ending 20 minutes after news articles become publicly available.

Gidófalvi, G.; Elkan, C.: Using News Articles to Predict Stock Price Movements. Technical Report, Department of Computer Science and Engineering. University of California, San Diego., 2003-03-26.

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