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This paper has employed a data mining approach for Going Concern Prediction (GCP) for one year ahead and has applied Classification and Regression Tree (CART) and Naïve Bayes Bayesian Network (NBBN) based on feature selection method in Iranian firms listed in Tehran Stock Exchange (TSE). For this purpose, at the first step, using the Stepwise Discriminant Analysis (SDA) has opted the final variables from among of 42 variables and in the next stage, has applied 10-fold cross-validation to figure out the optimal model. Mc Nemar test signifies that there is a significant difference between the two models in terms of prediction accuracy and CART model is able to predict going concern more accurately. The CART model reached 99.92 and 98.62 percent accuracy rates so as to training and holdout data.
Dr. Mahdi Salehi, Fezeh Zahedi Fard. 2013. "Data Mining Approach to Prediction of Going Concern Using Classification and Regression Tree (CART)". Global Journal of Management and Business Research - D: Accounting & Auditing GJMBR-D Volume 13 (GJMBR Volume 13 Issue 8 (D3)).
Crossref Journal DOI 10.17406/GJMBR
Print ISSN 0975-5853
e-ISSN 2249-4588
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Total Score: 87
Country: Iran
Subject: Global Journal of Management and Business Research - D: Accounting & Auditing
Authors: Dr. Mahdi Salehi, Fezeh Zahedi Fard (PhD/Dr. count: 1)
View Count (all-time): 495
Total Views (Real + Logic): 1379
Total Downloads (simulated): 112
Publish Date: 2013 03, Wed
Monthly Totals (Real + Logic):
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