Automated news reading: Stock price prediction based on financial news using context-capturing features

Automated news reading: Stock price prediction based on financial news using context-capturing features

0.00 Avg rating0 Votes
Article ID: iaor20141654
Volume: 55
Issue: 3
Start Page Number: 685
End Page Number: 697
Publication Date: Jun 2013
Journal: Decision Support Systems
Authors: , ,
Keywords: newspapers, text processing, stock prices
Abstract:

We examine whether stock price prediction based on textual information in financial news can be improved as previous approaches only yield prediction accuracies close to guessing probability. Accordingly, we enhance existing text mining methods by using more expressive features to represent text and by employing market feedback as part of our feature selection process. We show that a robust feature selection allows lifting classification accuracies significantly above previous approaches when combined with complex feature types. This is because our approach allows selecting semantically relevant features and thus, reduces the problem of over‐fitting when applying a machine learning approach. We also demonstrate that our approach is highly profitable for trading in practice. The methodology can be transferred to any other application area providing textual information and corresponding effect data.

Reviews

Required fields are marked *. Your email address will not be published.