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http://ssrn.com/abstract=1836742
 
 

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Detection-Controlled Prediction of Accounting Irregularities: Channel Stuffing as an Illustrative Case


Somnath Das


University of Illinois at Chicago

Pervin K. Shroff


University of Minnesota - Twin Cities - Carlson School of Management

Haiwen Zhang


Ohio State University (OSU) - Department of Accounting & Management Information Systems

July 20, 2012


Abstract:     
A number of prior studies use a sample of detected accounting irregularities, e.g., SEC’s enforcement actions and class-action lawsuits, to predict the likelihood of accounting violations. The ability of these models to correctly identify potential violations is inhibited by the fact that all violations that occur are not observed. We demonstrate that using a partial-observability bivariate probit model to jointly estimate the probability of commission and the probability of detection of an accounting violation helps to improve the classification accuracy of the prediction model when not all violations are detected. As an illustrative example, we use a sample of firms that engaged in accounting irregularities associated with a particular form of revenue manipulation – channel stuffing. Channel stuffing is an ideal setting for this purpose because it is difficult to detect without the help of whistle-blowers and provides multiple ex post indicators that can help validate the model’s identification of potential violators. Our results show that the power and specification of the bivariate probit prediction model is superior to that of the simple probit model. We find that a sub-sample of the population of firms identified as having a high likelihood of channel stuffing by the bivariate probit model has similar industry representation and exhibits future reversals in sales, production and profitability that closely parallel those of the detected channel stuffing sample. The same is not true for the sub-sample identified by the simple probit model. In fact, the sub-samples identified by the two models do not exhibit significant overlap. Our results highlight the need to control for the probability of detection to minimize misclassification in studies predicting accounting irregularities that are hard to detect.

Number of Pages in PDF File: 46

Keywords: Channel Stuffing, Bivariate Probit Model with Partial Observability, Detection

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Date posted: May 10, 2011 ; Last revised: July 22, 2012

Suggested Citation

Das, Somnath and Shroff, Pervin K. and Zhang, Haiwen, Detection-Controlled Prediction of Accounting Irregularities: Channel Stuffing as an Illustrative Case (July 20, 2012). Available at SSRN: http://ssrn.com/abstract=1836742 or http://dx.doi.org/10.2139/ssrn.1836742

Contact Information

Somnath Das
University of Illinois at Chicago ( email )
601 South Morgan Street
Chicago, IL 60607
United States
312-996-4482 (Phone)
312-996-4520 (Fax)
Pervin K. Shroff
University of Minnesota - Twin Cities - Carlson School of Management ( email )
19th Avenue South
Minneapolis, MN 55455
United States
612-626-1570 (Fax)
Haiwen Zhang (Contact Author)
Ohio State University (OSU) - Department of Accounting & Management Information Systems ( email )
2100 Neil Avenue
Columbus, OH 43210
United States
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