Lost in Standardization: Revisiting Accounting-based Return Anomalies Using As-filed Financial Statement Data

Posted: 12 Mar 2021

See all articles by Kai Du

Kai Du

Pennsylvania State University

Steven J. Huddart

Pennsylvania State University, University Park - Department of Accounting

Daniel Jiang

University of Waterloo - School of Accounting and Finance

Date Written: September 1, 2020

Abstract

SEC-mandated, machine-readable structured filings, or “as-filed data,” are an alternative source to Compustat for companies’ accounting data. Discrepancies between as-filed and Compustat data, apparently a result of Compustat’s standardizations, affect inferences about the existence and magnitude of the accruals anomaly: accruals calculated from as-filed (Compustat) data do (do not) predict returns. This difference is greater for firms whose accruals contain more investment-related information. Trades of hedge funds (and, to a lesser extent, mutual funds) that download structured filings correlate with the as-filed accruals signal. Inferences about four other accounting-based anomalies are similarly affected by discrepancies between data sources.

Keywords: asset pricing tests, accruals anomaly, data aggregators, standardization, XBRL, data quality

Suggested Citation

Du, Kai and Huddart, Steven J. and Jiang, Xin, Lost in Standardization: Revisiting Accounting-based Return Anomalies Using As-filed Financial Statement Data (September 1, 2020). Available at SSRN: https://ssrn.com/abstract=3781979

Kai Du

Pennsylvania State University ( email )

University Park, PA 16802
United States

Steven J. Huddart (Contact Author)

Pennsylvania State University, University Park - Department of Accounting ( email )

University Park, PA 16802-3603
United States
814-863-0448 (Phone)

HOME PAGE: http://directory.smeal.psu.edu/sjh11

Xin Jiang

University of Waterloo - School of Accounting and Finance ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1 N2L 3G1
Canada

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