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Automated news reading: Stock price prediction based on financial news using context-capturing features
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 miningmethods by usingmore expressive features to represent text and byemployingmarket 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 problemof over-fittingwhen applying amachine 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
Keywords: Text mining Financial news Stock price prediction Decision support
Author(s): .
Source: Decision Support Systems 55 (2013) 685–697
Subject: مدیریت مالی
Category: مقاله مجله
Release Date: 2013
No of Pages: 13
Price(Tomans): 0
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