Accounting for Uncertainty: An Application of Bayesian Methods to Accruals Models

47 Pages Posted: 16 Jul 2019

See all articles by Matthias Breuer

Matthias Breuer

Columbia University

Harm H. Schütt

Tilburg University - Tilburg School of Economics and Management

Date Written: July 9, 2019

Abstract

We introduce a Bayesian approach for predicting “normal” accruals — a vital ingredient for measuring and identifying accrual-based earnings management. The literature’s standard approach takes a given model of normal accruals for granted, and neglects any prediction uncertainty. By contrast, our approach allows incorporating researchers’ uncertainty about the relevant models and parameters in the prediction of normal accruals. Our approach promises to increase power and reduce false positives in tests for opportunistic earnings management as a result of better predictions of normal accruals and more robust inferences. We advocate for greater use of Bayesian methods in accounting research, especially since they can now be easily implemented in popular statistical software packages.

Keywords: Accruals, Earnings Management, Prediction, Bayes

JEL Classification: C11, C53, M40

Suggested Citation

Breuer, Matthias and Schütt, Harm H., Accounting for Uncertainty: An Application of Bayesian Methods to Accruals Models (July 9, 2019). Available at SSRN: https://ssrn.com/abstract=3417406 or http://dx.doi.org/10.2139/ssrn.3417406

Matthias Breuer (Contact Author)

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

Harm H. Schütt

Tilburg University - Tilburg School of Economics and Management ( email )

PO Box 90153
Tilburg, 5000 LE Ti
Netherlands

Register to save articles to
your library

Register

Paper statistics

Downloads
106
Abstract Views
386
rank
256,603
PlumX Metrics