In English

Interday news-based prediction of stock prices and trading volume

Christian Söyland
Göteborg : Chalmers tekniska högskola, 2015. Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, ISSN 1652-8557; 2015:45, 2015.
[Examensarbete på avancerad nivå]

This thesis investigates the predictive power of online news on one-day stock price up or down changes and high or low trade volume of 19 major banks and nancial institutions within the MSCI World Index, during the period from January 1 2009 to April 16 2015. The news data correspond to news articles, press releases, and stock exchange information, and were obtained by a web-crawler, which scanned around 6000 online sources for news and saved them in a database. The news are partitioned and labeled into two classes according to which price change class, or trade volume class, it corresponds. A supervised automated document classi cation model is created and used for prediction. The model does not succeed in predicting the one-day stock price changes, but the percentage of correctly labeled documents in the one-day trade volume experiment was 78:3%, i.e. a classi cation accuracy of 78:3% was achieved, suggesting that online news does contain some valuable predictive information.

Nyckelord: stock price prediction, document classi cation, text mining, trade volume prediction, financial prediction, news analytics



Publikationen registrerades 2015-10-05.

CPL ID: 223682

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