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Predicting Auditor Changes Using Financial Distress Variables And The Multiple Criteria Linear Programming (MCLP) And Other Data Mining Approaches

Year: 2011

Type: article

Abstract: Our study evaluates a multiple criteria linear programming (MCLP) and other data mining approaches to predict auditor changes using a portfolio of financial statement measures to capture financial distress. The results of the MCLP approach and the other data mining approaches show that these methods perform reasonably well to predict auditor changes using financial distress variables. Overall accuracy rates are more than 60 percent, and true positive rates exceed 80 percent. Our study is designed to establish a starting point for auditor-change prediction using financial distress variables. Further research should incorporate additional explanatory variables and a longer study period to improve prediction rates.

Source: Journal of Applied Business Research (JABR)

DOI: 10.19030/jabr.v27i5.5597

Authors: Wikil Kwak, Susan Eldridge, Yong Shi, Gang Kou

Institutions: University of Nebraska at Omaha, University of Electronic Science and Technology of China

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: bronze

OpenAlex ID: W2175098095

FWCI: 2.946

Cites: 24

Cited by: 12

Related to: 10