PREDICTING AUDITOR’S OPINIONS: A NEURAL NETWORKS APPROACH

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POURHEYDARI OMID; AZAMI Z.

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Paper Information

Journal: JOURNAL OF ACCOUNTING KNOWLEDGE Year:2011 | Volume:1 | Issue:3 Page(s): 77-97

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Information Journal Paper

Title

PREDICTING AUDITOR’S OPINIONS: A NEURAL NETWORKS APPROACH

Author(s)

POURHEYDARI OMID | AZAMI Z. | Issue Writer Certificate

Keywords

AUDITOR’S OPINIONQ3

MULTILAYER PERCEPTRON NEURAL NETWORKSQ3

DATA MININGQ3

LOGISTIC REGRESSIONQ2

Abstract

Data mining methods can be used in order to facilitate auditors to issue their opinion. This paper initially applies two DATA MINING classification techniques to develop models capable of identifying AUDITOR’S OPINION in Iran. The techniques used are Multilayer Perceptron neural network and LOGISTIC REGRESSION. The period of this research is start of 2003 to end of 2009. The input vector is compose of financial data such as financial distress and non-financial data such as firm litigation. The four developed models are compared in terms of their performance. The results demonstrate the high explanatory power of the MLP model in identification of audit opinion. The model developed is accurate in classifying the total sample correctly with rate 87/75%. The model is also found to outperform traditional LOGISTIC REGRESSION. The result of this study can be useful to internal and external auditors, Investors, creditors, company decision-makers and other stakeholders.

Cites

FORECASTING THE TYPE OF AUDIT OPINIONS: A DATA MINING APPROACH

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APA: Copy

POURHEYDARI, OMID, & AZAMI, Z.. (2011). PREDICTING AUDITOR’S OPINIONS: A NEURAL NETWORKS APPROACH. JOURNAL OF ACCOUNTING KNOWLEDGE, 1(3), 77-97. SID. https://sid.ir/paper/163324/en

Vancouver: Copy

POURHEYDARI OMID, AZAMI Z.. PREDICTING AUDITOR’S OPINIONS: A NEURAL NETWORKS APPROACH. JOURNAL OF ACCOUNTING KNOWLEDGE[Internet]. 2011;1(3):77-97. Available from: https://sid.ir/paper/163324/en

IEEE: Copy

OMID POURHEYDARI, and Z. AZAMI, “PREDICTING AUDITOR’S OPINIONS: A NEURAL NETWORKS APPROACH,” JOURNAL OF ACCOUNTING KNOWLEDGE, vol. 1, no. 3, pp. 77–97, 2011, [Online]. Available: https://sid.ir/paper/163324/en

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