Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2009 | HLT-NAACL | text regression problem,past volatility,well-known regression technique,models rival past volatility,real-world continuous quantity,financial risk,sec-mandated financial report,available financial report,regression model,financial report,predicting risk |
Field | DocType | Citations |
Financial risk,Financial ratio,Regression,Computer science,Regression analysis,Financial analysis,Legislation,Empirical measure,Finance,Volatility (finance) | Conference | 65 |
PageRank | References | Authors |
4.26 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shimon Kogan | 1 | 67 | 6.60 |
| Dimitry Levin | 2 | 65 | 4.26 |
| Bryan R. Routledge | 3 | 730 | 45.95 |
| Jacob S. Sagi | 4 | 96 | 10.04 |
| Noah A. Smith | 5 | 5867 | 314.27 |