This Is AuburnAUrora

Show simple item record

A Bayesian Approach to Detect the Firms with Material Weakness in Internal Control

Metadata FieldValueLanguage
dc.creatorSimsek, Serhat
dc.creatorBayraktar, Engin
dc.creatorRagothaman, Srini
dc.creatorDag, Ali
dc.description.abstractCapturing of relevant patterns in company’s financial data and the implications on the reporting are important for various financial statement users to identify the triggers of the significant deficiencies and material weaknesses. The objective of this study is to construct a company-specific risk score for the companies’ internal weaknesses, as well as to uncover the conditional relations between the independent predictors of firms’ material weaknesses. To do so, Tree Augmented Naive Bayes (TAN) and Logistic Regression (LR) algorithms are employed to analyze the data obtained from COMPUSTAT (Research Insight) for one year before the Material Weakness in Internal Control (MWIC) disclosure on several operating and financial ratios such as total asset turnover, profitability, capital intensity, size, current ratio, and operating performance. The proposed TAN method provides novel information on the interactions among the predictors and the conditional probability of MWIC for a given set of relevant firm characteristics.en_US
dc.publisherInstitute of Industrial and Systems Engineers (IISE)en_US
dc.relation.ispartof2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018en_US
dc.rightsCopyright© (2018) by Institute of Industrial & Systems Engineers (IISE) All rights reserved.en_US
dc.subjectData mining, Machine learning, Bayesian belief network, Logistic regressionen_US
dc.titleA Bayesian Approach to Detect the Firms with Material Weakness in Internal Controlen_US
dc.type.genreConference Proceedingen_US

Files in this item

This item appears in the following Collection(s)

Show simple item record