LDA quantification of 10-K Risk-Factors and the Information Content of Textual Reporting

51 Pages Posted: 16 Jun 2020

See all articles by Bruce D. Grundy

Bruce D. Grundy

University of Melbourne

Stefan Petry

University of Manchester - Alliance Manchester Business School

Date Written: May 23, 2020

Abstract

We use machine-learning to determine the information content of the Item 1A Risk Factors section of S&P 1500 10-Ks. We identify and quantify 30 risk-factors and show a strong positive relation between levels of and contemporaneous changes in risk-factors and proxies for the associated risks. Typically, 28% of cross-firm variation in a risk-proxy is explained by cross-firm variation in the associated risk-factor. Risk disclosure is not found to be forward-looking. Item 1A’s informativeness has not declined through time despite previously documented increases in boilerplate content, stickiness and redundancy. Indices of operating and financing risk help explain asset and equity volatility.

Keywords: risk-factors, textual analysis, 10-K, risk indices

JEL Classification: G10, G38, K22, M41

Suggested Citation

Grundy, Bruce D. and Petry, Stefan, LDA quantification of 10-K Risk-Factors and the Information Content of Textual Reporting (May 23, 2020). Available at SSRN: https://ssrn.com/abstract=3608594 or http://dx.doi.org/10.2139/ssrn.3608594

Bruce D. Grundy (Contact Author)

University of Melbourne ( email )

Faculty of Economics & Commerce
Department of Finance
Victoria, 3010
Australia
+61 3 8344 9083 (Phone)
+61 3 8344 6914 (Fax)

Stefan Petry

University of Manchester - Alliance Manchester Business School ( email )

Booth Street West
Manchester, M15 6PB
United Kingdom

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