Predicting Material Weaknesses In Internal Control Systems After The Sarbanes-Oxley Act Using Multiple Criteria Linear Programming And Other Data Mining Approaches
Authors: Kwak Wikil , Eldridge Susan , Shi Yong , Kou Gang 
Published: February 2016

<h1 style="TEXT-JUSTIFY: inter-ideograph; TEXT-ALIGN: justify; MARGIN: 0in 0.5in 0pt"><span style="font-family: Times New Roman;"><span style="COLOR: black; FONT-SIZE: 10pt">Our study proposes a multiple criteria linear programming (MCLP) </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">and other data mining </span><span style="COLOR: black; FONT-SIZE: 10pt">method</span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">s</span><span style="COLOR: black; FONT-SIZE: 10pt"> to predict </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">material weaknesses in a firm&rsquo;s internal control system after the Sarbanes-Oxley Act</span><span style="COLOR: black; FONT-SIZE: 10pt"> (SOX) using </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">2003-2004</span><span style="COLOR: black; FONT-SIZE: 10pt"> </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">U.S. </span><span style="COLOR: black; FONT-SIZE: 10pt">data.<span style="mso-spacerun: yes">&nbsp; </span>The results of the MCLP </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">and other data mining </span><span style="COLOR: black; FONT-SIZE: 10pt">approaches in </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">our</span><span style="COLOR: black; FONT-SIZE: 10pt"> </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">prediction </span><span style="COLOR: black; FONT-SIZE: 10pt">study show that the </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">MCLP</span><span style="COLOR: black; FONT-SIZE: 10pt"> method performs</span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO"> </span><span style="COLOR: black; FONT-SIZE: 10pt">better overall than the </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">other data mining approaches </span><span style="COLOR: black; FONT-SIZE: 10pt">using financial </span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO">and other </span><span style="COLOR: black; FONT-SIZE: 10pt">data from the Form 10-K report.</span><span style="COLOR: black; FONT-SIZE: 10pt; mso-fareast-language: KO"><span style="mso-spacerun: yes">&nbsp; </span>Consistent with prior research, firms that disclosed material weaknesses in their SOX Section 302 disclosures were more complex (based on the existence of foreign currency translations), more often used Big 4 auditors, and had lower operating cash flows-to-total assets ratios than the non-material weakness control firms.<span style="mso-spacerun: yes">&nbsp; </span>Because of mixed results on several profitability measures and marginal predictive ability for the MCLP and other methods used, more research is needed to identify firm characteristics that help investors, auditors, and others predict material weaknesses.</span></span></h1>