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Title: Abnormal accrual estimation: an automation data analysis technique

Authors: Francesca Rossignoli; Nicola Tommasi

Addresses: Department of Management, University of Verona, Italy, Via Cantarane 24, 37129, Verona, Italy ' Interdepartmental Centre of Economic Documentation (C.I.D.E.), University of Verona, Italy, Via Cantarane 24, 37129, Verona, Italy

Abstract: 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.

Keywords: abnormal estimation; abnormal accrual; earnings management; prediction model; financial accounting.

DOI: 10.1504/IJDATS.2025.144576

International Journal of Data Analysis Techniques and Strategies, 2025 Vol.17 No.5, pp.1 - 18

Received: 01 Jul 2024
Accepted: 23 Sep 2024
Published online: 21 Feb 2025 *