Detecting automotive and parts manufacturing industry earnings management by combining Bayesian networks and C5. 0 decision tree

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Saeidmoghadam Marziyeh; JAVID DARUSH; HEMATFAR MAHMMOD

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Journal: FINANCIAL ACCOUNTING Year:2018 | Volume:9 | Issue:36 Page(s): 101-125

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Information Journal Paper

Title

Detecting automotive and parts manufacturing industry earnings management by combining Bayesian networks and C5. 0 decision tree

Author(s)

Saeidmoghadam Marziyeh | JAVID DARUSH | HEMATFAR MAHMMOD | Issue Writer Certificate

Keywords

Accruals‚ Bayesian networks‚ C5. 0 decision tree‚ Data mining‚ Earnings managementQ2

Abstract

Abstract: Investors, shareholders, financial analysts and other stakeholders pay special attention to the figure of profit reported in financial statements as one of the main factors in decision-making. Hence the recognition of earnings management in the financial statements is very important. The main objective of this research is to investigate the effectiveness a model to specify the earnings management level by combining two data-mining techniques including a Bayesian networks and a decision tree. To do so, 24 variables have been used as the independent variables affecting earnings management and discretionary accruals as the dependent variables replacing earnings management. In this study the recommended method by Dechow et al (2012) was used to measure the earnings management. The research's statistical population included listed companies on the Tehran stock exchange, active in automotive and parts manufacturing industry and device constructions during years 88-93. The research findings indicate that the hybrid model of Bayesian networks with the C5. 0 decision tree has a high capability to characterize the level of the earnings management in the automotive and parts manufacturing industry. The most important known variable by the hybrid model to identify the level of the earnings management in the automotive and parts manufacturing industry is performance threshold variable with the significance ratio of 100%.

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APA: Copy

Saeidmoghadam, Marziyeh, JAVID, DARUSH, & HEMATFAR, MAHMMOD. (2018). Detecting automotive and parts manufacturing industry earnings management by combining Bayesian networks and C5. 0 decision tree. FINANCIAL ACCOUNTING, 9(36 ), 101-125. SID. https://sid.ir/paper/168099/en

Vancouver: Copy

Saeidmoghadam Marziyeh, JAVID DARUSH, HEMATFAR MAHMMOD. Detecting automotive and parts manufacturing industry earnings management by combining Bayesian networks and C5. 0 decision tree. FINANCIAL ACCOUNTING[Internet]. 2018;9(36 ):101-125. Available from: https://sid.ir/paper/168099/en

IEEE: Copy

Marziyeh Saeidmoghadam, DARUSH JAVID, and MAHMMOD HEMATFAR, “Detecting automotive and parts manufacturing industry earnings management by combining Bayesian networks and C5. 0 decision tree,” FINANCIAL ACCOUNTING, vol. 9, no. 36 , pp. 101–125, 2018, [Online]. Available: https://sid.ir/paper/168099/en

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