Predicting abnormal returns from news using text classification
DOI: 10.1080/14697688.2012.672762
Title: Predicting abnormal returns from news using text classification
Journal Title: Quantitative Finance
Volume: pages 1-14
Issue: pages 1-14
Publication Date: pages 1-14
Start Page: pages1-14
End Page: pages1-14
ISSN: 1469-7688
Author: Ronny Lussa* & Alexandre D'Aspremonta
a ORFE Department , Princeton University , Princeton , NJ, 08544 , USA
Abstract: We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features to increase classification performance and we develop an analytic center cutting plane method to solve the kernel learning problem efficiently. We observe that while the direction of returns is not predictable using either text or returns, their size is, with text features producing significantly better performance than historical returns alone.
Accepted: 28 Feb 2012

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