Data Mining Approach to Prediction of Going Concern Using Classification and Regression Tree (CART)

Data Mining Approach to Prediction of Going Concern Using Classification and Regression Tree (CART)

dr._mahdi_salehi

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Dr. Mahdi Salehi

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Fezeh Zahedi Fard

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Ferdowsi University of Mashhad

Data Mining Approach to Prediction of Going Concern Using Classification and Regression Tree (CART)

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Abstract

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. McNemar 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.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

dr._mahdi_salehi. 2013. \u201cData Mining Approach to Prediction of Going Concern Using Classification and Regression Tree (CART)\u201d. Global Journal of Management and Business Research - D: Accounting & Auditing GJMBR-D Volume 13 (GJMBR Volume 13 Issue D3).

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GJMBR Volume 13 Issue D3

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Journal Specifications

Crossref Journal DOI 10.17406/GJMBR

Print ISSN 0975-5853

e-ISSN 2249-4588

Keywords

classification and regression tree Data mining financial ratios going concern prediction Iran naïve bayes bayesian network

Submission ReceivedDecember 10, 2012
Peer Review Double Blind
Handling Editor
Accepted January 2, 2013
Published January 15, 2013

Version of record

v1.2

Issue date

April 23, 2013

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Article Matrices

Total Score: 107

Country: Unknown

Subject: Global Journal of Management and Business Research - D: Accounting & Auditing

Authors: Dr. Mahdi Salehi, Fezeh Zahedi Fard (PhD/Dr. count: 1)

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Total Views: 5184

Total Downloads: 2495

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