Abnormal accrual estimation: an automation data analysis technique

Accounting studies rely on predictive analytics to estimate abnormal accruals as indicators of managerial opportunism. Abnormal accruals are estimated by running predictive models and manually imposing a combination of conditions to select the control sample. This process is executed using loops where the estimation is repeated over the control observations meeting the combined conditions. The recursive estimation generates several inefficiencies. We provide a technique to estimate abnormal measures by automatising: i) the estimation of the predictive model; and ii) the selection of the control sample according to multiple procedures. The command offers a unique information set about the estimation results and process. We illustrate the use of abnormalest through empirical applications. We compare the accuracy of predictions under different approaches and models. The command abnormalest allows to overcome the inefficiencies, provides a unique set of information about the estimation, and is extendible to every social science.