Using Unstructured and Qualitative Disclosures to Explain Accruals

60 Pages Posted: 15 Feb 2015 Last revised: 22 Jul 2015

See all articles by Richard M. Frankel

Richard M. Frankel

Washington University in Saint Louis - Olin Business School

Jared N. Jennings

Washington University in St. Louis

Joshua A. Lee

University of Georgia

Date Written: May 15, 2015

Abstract

We use MD&A disclosures to predict current-year firm-level accruals using support-vector regressions. We call these predictions big-data accruals. Our aim is to measure the explanatory power of MD&A disclosures for liquidity and critical accounting choices. We find that big-data accruals explain a statistically and economically significant portion of firm-level accruals and identify more persistent accruals. They have less incremental explanatory power when 10-K readability is low and more when earnings are more difficult to predict using fundamentals. In addition, we find that big data accruals are incrementally useful in predicting next period’s cash flows. We apply our technique to conference calls and find that they have similar explanatory power for accruals. Our technique can be applied to a variety of unstructured and qualitative disclosures to assess their narrative content.

Keywords: Textual Analysis; Big Data; Accruals

JEL Classification: M40; M41

Suggested Citation

Frankel, Richard M. and Jennings, Jared N. and Lee, Joshua A., Using Unstructured and Qualitative Disclosures to Explain Accruals (May 15, 2015). Available at SSRN: https://ssrn.com/abstract=2563940 or http://dx.doi.org/10.2139/ssrn.2563940

Richard M. Frankel

Washington University in Saint Louis - Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Jared N. Jennings (Contact Author)

Washington University in St. Louis ( email )

One Brookings Drive
Campus Box 1208
Saint Louis, MO MO 63130-4899
United States

Joshua A. Lee

University of Georgia ( email )

Athens, GA
United States

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