Paper: Predicting Risk from Financial Reports with Regression

ACL ID N09-1031
Title Predicting Risk from Financial Reports with Regression
Venue Human Language Technologies
Session Main Conference
Year 2009

We address a text regression problem: given a piece of text, predict a real-world continuous quantity associated with the text’s meaning. In this work, the text is an SEC-mandated finan- cial report published annually by a publicly- traded company, and the quantity to be pre- dicted is volatility of stock returns, an empiri- cal measure of financial risk. We apply well- known regression techniques to a large cor- pus of freely available financial reports, con- structing regression models of volatility for the period following a report. Our models ri- val past volatility (a strong baseline) in pre- dicting the target variable, and a single model that uses both can significantly outperform past volatility. Interestingly, our approach is more accurate for reports after the passage of the Sa...