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 financial report published annually by a publicly-traded company, and the quantity to be predicted is volatility of stock returns, an empirical measure of financial risk. We apply well-known regression techniques to a large corpus of freely available financial reports, constructing regression models of volatility for the period following a report. Our models rival past volatility (a strong baseline) in predicting 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 Sarbanes-Oxley Act of 2002, giving some evidence for the success of that legislation in making financial reports more informative.
Predicting Risk from Financial Reports with Regression
Shimon Kogan,Dimitry Levin,Bryan R. Routledge,Jacob S. Sagi,Noah A. Smith
Published 2009 in North American Chapter of the Association for Computational Linguistics
ABSTRACT
PUBLICATION RECORD
- Publication year
2009
- Venue
North American Chapter of the Association for Computational Linguistics
- Publication date
2009-05-31
- Fields of study
Business, Economics, Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
- No claims are published for this paper.
CONCEPTS
- No concepts are published for this paper.
REFERENCES
Showing 1-31 of 31 references · Page 1 of 1