title: A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.
abstract: As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.
authors.affiliation: Department of Accounting Information, National Taipei University of Business, 321 Jinan Road, Section 1, Taipei 10051, Taiwan.
authors.forename: Suduan
authors.identifier: 0000-0003-3430-7281
authors.initials: S
authors.lastname: Chen
authors.affiliation: Department of Business Administration, National Taipei University, No. 67, Section 3, Ming-shen East Road, Taipei 10478, Taiwan.
authors.forename: Yeong-Jia James
authors.initials: YJ
authors.lastname: Goo
authors.affiliation: Department of Business Administration, National Taipei University, No. 67, Section 3, Ming-shen East Road, Taipei 10478, Taiwan.
authors.forename: Zone-De
authors.initials: ZD
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D003663:Decision Trees; D005376:Financial Management; D005544:Forecasting / Q000379:methods*; D005607:Fraud / Q000517:prevention & control / Q000639:trends*; D006801:Humans; D016015:Logistic Models; D060388:Support Vector Machine