Industrial and Systems Engineeringhttps://aurora.auburn.edu/handle/11200/442582024-03-28T22:31:36Z2024-03-28T22:31:36ZA Bayesian Approach to Detect the Firms with Material Weakness in Internal Controlhttps://aurora.auburn.edu/handle/11200/494362019-08-01T15:43:58ZA Bayesian Approach to Detect the Firms with Material Weakness in Internal Control
Capturing 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.