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• W2175098095 abstract "<span style=font-family: Times New Roman; font-size: small;> </span><h1 style=margin: 0in 0.5in 0pt; text-align: justify; page-break-after: auto; mso-pagination: none;><span style=font-family: Times New Roman;><span style=color: black; font-size: 10pt; mso-themecolor: text1;>Our study evaluates a multiple criteria linear programming (MCLP) </span><span style=color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;>and other </span><span style=color: black; font-size: 10pt; mso-themecolor: text1;>data mining approach</span><span style=color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;>es</span><span style=color: black; font-size: 10pt; mso-themecolor: text1;> </span><span style=color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;>to predict auditor changes using a portfolio of financial statement measures to capture financial distress</span><span style=color: black; font-size: 10pt; mso-themecolor: text1;>.<span style=mso-spacerun: yes;> </span>The results of the MCLP approach and the other data mining approaches show that these methods perform</span><span style=color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;> reasonably well to predict auditor changes </span><span style=color: black; font-size: 10pt; mso-themecolor: text1;>using financial distress variables.</span><span style=color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;><span style=mso-spacerun: yes;> </span>Overall accuracy rates are more than 60 percent, and true positive rates exceed 80 percent.<span style=mso-spacerun: yes;> </span>Our study is designed to establish a starting point for auditor-change prediction using financial distress variables.<span style=mso-spacerun: yes;> </span>Further research should incorporate additional explanatory variables and a longer study period to improve prediction rates.</span></span></h1><span style=font-family: Times New Roman; font-size: small;> </span>" @default.
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• W2175098095 date "2011-08-09" @default.
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• W2175098095 title "Predicting Auditor Changes Using Financial Distress Variables And The Multiple Criteria Linear Programming (MCLP) And Other Data Mining Approaches" @default.
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