Abstract
Research background: Even though unintentional accounting errors leading to financial restatements look like less serious distortion of publicly available information, it has been shown that financial restatements impacts on financial markets are similar to intentional fraudulent activities. Unintentional accounting errors leading to financial restatements then affect value of company shares in the short run which negatively impacts all shareholders.
 Purpose of the article: The aim of this manuscript is to predict unintentional accounting errors leading to financial restatements based on information from financial statements of companies. The manuscript analysis if financial statements include sufficient information which would allow detection of unintentional accounting errors.
 Methods: Method of classification and regression trees (decision tree) and random forest have been used in this manuscript to fulfill the aim of this manuscript. Data sample has consisted of 400 items from financial statements of 80 selected international companies. The results of developed prediction models have been compared and explained based on their accuracy, sensitivity, specificity, precision and F1 score. Statistical relationship among variables has been tested by correlation analysis. Differences between the group of companies with and without unintentional accounting error have been tested by means of Kruskal-Wallis test. Differences among the models have been tested by Levene and T-tests.
 Findings & value added: The results of the analysis have provided evidence that it is possible to detect unintentional accounting errors with high levels of accuracy based on financial ratios (rather than the Beneish variables) and by application of random forest method (rather than classification and regression tree method).
Highlights
Accounting fraud is an intentional attempt to deceive or mislead users of publicly available financial information, investors and creditors, by preparing and disclosing manipulated financial statements (Rezaee, 2005)
Findings & value added: The results of the analysis have provided evidence that it is possible to detect unintentional accounting errors with high levels of accuracy based on financial ratios and by application of random forest method
Sosnowski (2017) has showed that new stock companies used an aggressive earnings management strategy to increase additional level of financing before process of initial public offering (IPO). The aim of this manuscript is to predict unintentional accounting errors leading to financial restatements based on information from financial statements of companies
Summary
Accounting fraud is an intentional attempt to deceive or mislead users of publicly available financial information, investors and creditors, by preparing and disclosing manipulated financial statements (Rezaee, 2005). Accounting frauds have been studied by many authors from different perspectives Authors such as Beneish et al (1999) have built own simple formulas for prediction of fraudulent financial statements similar to bankruptcy models by Altman (1968). Kotsiantis et al (2006), Ravisankar et al (2011) Liu et al (2015) have used various different data-mining techniques for detection of fraudulent companies based on financial statement data Other authors such as Humpherys et al (2011) and Throckmorton et al (2015) have tested prediction of fraudulent companies based on linguistic variables in annual reports without using any financial data from these statements. Other authors, such as Ibadin and Ehigie (2019), Paseková et al (2019) or Homola and Paseková (2020), have studied relationship between particular accounting standard and occurrence of accounting fraud or misstatement in companies reporting under this standard
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