Developing a local model to detect the unethical behavior of managers in financial reporting

People face moral dilemmas in their lives. Managers, accountants, and auditors also face a variety of situations in their careers, situations that tempt them to engage in unethical behavior. One of these unethical behaviors is fraud and earnings manipulation, which leads to ethical failure in companies. Given the potential for financial statement fraud and its detrimental effects on the economy, preventing and detecting financial fraud is important to prevent its disastrous consequences. Therefore, the introduction of an early detection tool is essential to alert executive bodies to further investigation or legal action. Developing a model through which to predict earnings manipulation and detect the unethical behavior of managers, allows for a better evaluation of the company's performance. The purpose of this study is to investigate the ability of Beneish and Spathis models to detect fraudulent financial reporting. Then the coefficients of these two models were adjusted using logistic regression and the ability of these two new adjusted models to detect fraudulent financial reporting was investigated. In this regard, the data of 99 manufacturing companies listed on the Tehran Stock Exchange (1089 observations) during the years 2008 – 2018 were reviewed. The results show that the initial model of Beneish and Spathis does not have good power in detecting fraudulent financial reporting, but the adjusted model of Beneish and the adjusted model of Spathis are able to detect fraudulent financial reporting with 83% and 82% accuracy, respectively.