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Predicting Risk from Financial Reports with Regression

By Shimon Kogan (5404886), Dimitry Levin (5457569), Bryan R Routledge (5361950), Jacob S. Sagi (5457572) and Noah A. Smith (663492)


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 publiclytraded company, and the quantity to be predicted is volatility of stock returns, an empirical measure of financial risk. We apply wellknown 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

Topics: Business and Management not elsewhere classified, Business, Management
Year: 2018
DOI identifier: 10.1184/r1/6707432.v1
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Provided by: KiltHub
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