Affiliation:
1. Iona College, New Rochelle, NY
2. University of Arizona, Tucson, AZ
Abstract
Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities. Through this approach, we investigated 9,211 financial news articles and 10,259,042 stock quotes covering the S&P 500 stocks during a five week period. We applied our analysis to estimate a discrete stock price twenty minutes after a news article was released. Using a support vector machine (SVM) derivative specially tailored for discrete numeric prediction and models containing different stock-specific variables, we show that the model containing both article terms and stock price at the time of article release had the best performance in closeness to the actual future stock price (MSE 0.04261), the same direction of price movement as the future price (57.1% directional accuracy) and the highest return using a simulated trading engine (2.06% return). We further investigated the different textual representations and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Reference28 articles.
1. Bishop C. M. and Tipping M. E. 2003. Bayesian Regression and Classification. IOS Press Amsterdam. Bishop C. M. and Tipping M. E. 2003. Bayesian Regression and Classification. IOS Press Amsterdam.
2. Burns D. and Wutkowski K. Nov. 15 2005. Schwab to miss forecast fined by NYSE. http://biz.yahoo.com/rb/051115/financial_schwab.html?.v=3. Burns D. and Wutkowski K. Nov. 15 2005. Schwab to miss forecast fined by NYSE. http://biz.yahoo.com/rb/051115/financial_schwab.html?.v=3.
3. Cho V. Wuthrich B. and Zhang J. 1998. Text processing for classification. J. Computat. Intel. Fin. 26. Cho V. Wuthrich B. and Zhang J. 1998. Text processing for classification. J. Computat. Intel. Fin. 26.
4. Early user---system interaction for database selection in massive domain-specific online environments
Cited by
453 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献