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[[テキスト分析]]
- 2001年版 http://www-cse.ucsd.edu/~gyozo/studies/cse254_AI/spring01/stock_price_prediction.pdf
- 2003年版 http://www.cs.aau.dk/~gyg/docs/financial-prediction-TR.pdf
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
gyozo@cs.ucsd.edu
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.
http://www.cs.aau.dk/~gyg/docs/financial-prediction-TR.pdf, 2003-03-26.
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