Detection-Controlled Prediction of Accounting Irregularities: Channel Stuffing as an Illustrative Case
46 Pages Posted: 10 May 2011 Last revised: 22 Jul 2012
Date Written: July 20, 2012
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.
Keywords: Channel Stuffing, Bivariate Probit Model with Partial Observability, Detection
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