Interday news-based prediction of stock prices and trading volume
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Typ
Examensarbete för masterexamen
Master Thesis
Master Thesis
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2015
Författare
Söyland, Christian
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Annan teknik , Other Engineering and Technologies