The Detection of Profit Manipulation: Assessing the Ability of the Accrual and Revenue Models

Introduction

In recent decades, studies of financial information manipulation have been the focus of analysts and researchers. The recent requirements of the US Securities and Exchange Commission to scrutinize the discovery of fraudulent profits have prompted an investigation. However, the scientific and professional literature on profit manipulation detection is very limited. In the accounting literature, the difference between earnings management and earnings manipulation is not clearly defined, and the distinction between them must be explained (Impink, 2010).

Accounting profit is an important performance indicator used in many economic decisions such as stock valuation, performance appraisal, manager remuneration and profit sharing. This decision can be effective in transferring resources among people, so it causes to pay special attention to the capital market. In a conflict of interest environment, such as a conflict of interest between shareholders and managers, it is always possible for managers to try to manipulate profits. Earnings management is an activity that can be related to various outcomes from a full disclosure perspective in financial reporting. The uncertain nature of this threat and the many consequences of earnings management have forced researchers to measure and identify signs of earnings management and therefore provide models, which are often designed based on accounting variables due to the accounting variables used in earnings management (Taleb Nia and Safari Sarchah, 2014).

Copeland (1968) introduced profit manipulation as the relative ability of managers to reduce or increase reported earnings. The intentional actions of company managers to bring reported profits closer to the intended level, which is classified into three categories in terms of compliance with accounting rules and standards: earnings management, falsification (Merchant, 1990),and Creative Accounting Practices (CAP). Earnings management involves postponing the period affected by an operation by changing measurement methods, accelerating sales, or delaying purchases. Here, we can limit background earnings management to manipulation of accounting figures, not to profit manipulation that includes performance in real business situations. Falsification refers to exposing incorrect data. In this case, such acts may be considered criminal. However, the thin line between the two manipulations remains unclear. The creative accounting is often used to describe the process by which accounting professionals use their knowledge to manipulate the figures in annual accounts (Shah and Butt, 2011).

The purpose of this study is to review mathematical methods using for the detection of profit manipulation and compare the average profit management and ability of accruals and revenue models to predict earnings management in companies.

Accruals Models

In the literature on earnings management, manipulations that are explications of standards are mainly discussed, for example, accrual decision making and most research has shown that discretionary accrual models can be an effective tool for identifying earnings manipulation because they can show unusual figures associated with reports and predicted values. ​​Some previous studies on earnings management emphasized on how accounts are manipulated through accruals (DeAngelo, 1986; Healy, 1985). Management of accruals refers to changes in estimates such as useful lives, the possibility of repaying debtors, and other accruals at the end of the year in an effort to change reported earnings to achieve a target. DeAngelo (1986) used the total accruals of the past period, which are scaled by lagged total assets, as a measure of nondiscretionary accruals. Haley (1985) applied the average of total accruals that have been scaled by lagged total assets over the estimated period as a measure of nondiscretionary accruals.