Predicting Abnormal Returns From News Using Text Classification
Machine Learning
2009-06-24 v3 Artificial Intelligence
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.
Cite
@article{arxiv.0809.2792,
title = {Predicting Abnormal Returns From News Using Text Classification},
author = {Ronny Luss and Alexandre d'Aspremont},
journal= {arXiv preprint arXiv:0809.2792},
year = {2009}
}
Comments
Larger data sets, results on time of day effect, and use of delta hedged covered call options to trade on daily predictions