A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

SuduanChen; Yeong-Jia JamesGoo; Zone-DeShen

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A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

机译：一种逐步回归，逻辑回归，支持向量机和决策树的混合方法，用于预测欺诈性财务报表

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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%.

机译：随着企业的欺诈性财务报表越来越严重，每个通过日越来越严重，建立一个有效的预测欺诈性财务报表模式，企业已成为学术研究和财务实践的重要问题。在使用逐步回归筛选重要变量之后，该研究还匹配了逻辑回归，支持向量机和决策树来构建分类模型以进行比较。该研究采用金融和非金融变量来协助建立预测欺诈财务报表模式。研究对象是1998年至2012年之间发生了欺诈和非自裁财务报表的公司。该调查结果是，财务和非金融信息有效地用于区分欺诈性财务报表，决策树C5.0具有最佳的分类效应85.71 ％。

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《ScientificWorldJournal》 |2014年第3期|共9页
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SuduanChen; Yeong-Jia JamesGoo; Zone-DeShen;
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A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

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