Firm risk identification through topic analysis of textual financial disclosures

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Corporate risk disclosures as part of U.S. public companies' financial reports are mandated by the Securities and Exchange Commission (SEC) since 2005. It provides forward-looking information about companies' future business and potential risks. This study analyzes risk types revealed in these risk disclosures and examines their potential implications on stock returns. Using 16,110 risk disclosures submitted to the SEC from 2011 to 2015, we apply Sentence Latent Dirichlet Allocation (Sent-LDA) model to infer risk types and propose a novel algorithm to match new factors with existing risk types which generates 90% correct matches. We then quantify the impact of different risk factors on the distribution of stock returns using different time windows. We find that common risk factors, such as accounting risk and acquisition risk, have significant effects on both long-term and short-term stock returns. Some other factors only have short-term or long-term effects on stock returns. These findings provide evidence that the companies' self-disclosed risk factors have significant impacts on subsequent stock return volatility and such impacts can be used to predict potential stock change after the public release of the financial risk disclosures.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
ISBN (Electronic)9781509042401
DOIs
StatePublished - 9 Feb 2017
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Publication series

Name2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/TerritoryGreece
CityAthens
Period6/12/169/12/16

Fingerprint

Dive into the research topics of 'Firm risk identification through topic analysis of textual financial disclosures'. Together they form a unique fingerprint.

Cite this