Interday news-based prediction of stock prices and trading volume

Abstract

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.