Detection of Channel Stuffing

Based on a sample of firms that engaged in channel stuffing, we develop a model that predicts the probability of channel stuffing behavior in a broad cross-section of firms. Channel stuffing leads to accelerated revenue recognition by managing "real activities to achieve short-term revenue and earnings targets. Given that channel stuffing is difficult to detect without the help of whistle-blowers, we control for undetected cases by estimating a bivariate probit model with partial observability. The model simultaneously estimates the effect of incentives, opportunities, and financial performance measures on the probability that a firm engages in channel stuffing and the probability that the channel stuffing activity is detected. Our results show that smaller firms, firms with higher growth opportunities, higher profit margins, and limited accrual management ability are more likely to engage in channel stuffing. A slowdown in receivables collection in the affected quarter serves as a significant indictor of channel stuffing. At the same time, we find that firm size, institutional holdings, Big-4 auditor, and tighter accounting regulation increase the detection probability and in turn reduce the probability of channel stuffing. Further analysis shows that firms that engage in channel-stuffing experience declining sales, production and profitability in future periods, suggesting that this activity achieves short-term benefits only at the price of long-term adverse consequences. Our results show that the power and specification of the bivariate probit prediction model is superior to that of the simple probit model. In an ex post validation analysis, 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 (but not by the simple probit model) exhibits future performance reversals that closely parallel those of the actual channel stuffing sample. These results highlight the need to control for the probability of detection to minimize misclassification in studies predicting accounting irregularities that are hard to detect.