Mixture and Continuous 'Discontinuity' Hypotheses: An Earnings Management Model with Auditor-Required Adjustment

55 Pages Posted: 17 May 2012 Last revised: 9 Oct 2014

See all articles by Andrew Yim

Andrew Yim

Bayes Business School, City, University of London

Date Written: October 8, 2014


A parsimonious model is developed to understand two perplexing, salient features of the distributions of earnings, earnings change, and earnings surprise. The model provides guidance for empirical work to uncover the unmanaged earnings important to firm valuation and public scrutiny, yet unobserved by outside parties. Simulation results based on the model show that the puzzling volcano shape of the distributions can arise from the mixture of a spiky distribution of managed earnings with a bell-shaped distribution of unmanaged earnings. The spiky distribution is due to cookie-jar earnings management that compresses unmanaged earnings from both sides toward an earnings benchmark, leading to a concentration of density around there. The mixture is due to the auditor’s adjustment decision, which seems stochastic from the public’s or client firm’s perspective. Additional simulation results suggest that the widely documented discontinuity in the distributions can be partly due to a steep increase in density appearing like a discontinuity when a continuous distribution is plotted in terms of frequency counts in histogram bins. The main analytical results are a general characterization of the optimal earnings management strategy and the derivation of closed-form solutions for particular functional form assumptions. Potential applications include structurally estimating the model for policy analysis to assess the impact on earnings manipulation.

Keywords: Misreporting, Earnings Manipulation, Cookie-jar Accounting, Benchmark Reference, Auditor-client Interaction

JEL Classification: M43, M49, K42

Suggested Citation

Yim, Andrew, Mixture and Continuous 'Discontinuity' Hypotheses: An Earnings Management Model with Auditor-Required Adjustment (October 8, 2014). Available at SSRN: https://ssrn.com/abstract=2061381 or http://dx.doi.org/10.2139/ssrn.2061381

Andrew Yim (Contact Author)

Bayes Business School, City, University of London ( email )

Faculty of Finance
106 Bunhill Row
London, EC1Y 8TZ
United Kingdom

HOME PAGE: http://www.bayes.city.ac.uk/faculties-and-research/experts/andrew-yim

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