Lib-SVMs Detection Model of Regulating-Profits Financial Statement Fraud Using Data of Chinese Listed Companies

Author

Li Xiuzhi ; Ying Shuangshuang

Author_Institution

Sch. of Manage., China Univ. of Min. & Technol., Xuzhou, China

fYear

2010

fDate

7-9 Nov. 2010

Firstpage

1

Lastpage

4

Abstract

This paper uses Lib-SVM algorithm of RBF kernel and linear kernel to develop a model for detecting regulating-profits financial statement fraud with the data of 112 Chinese listed companies. It turns out that the prediction accuracy of Lib-SVM algorithm for RBF kernel function model is 86.667%, the overall accuracy is 87.5%. And the prediction accuracy of the Lib-SVM linear kernel function model is 83.333%, the overall accuracy rate is 86.612%. With the same samples, a Logistic regression model is developed, and the corresponding accuracy is 80% and 83.036%. The study reinforces the validity and efficiency of Lib-SVM algorithm as a research tool and provides additional empirical evidence regarding the merits of suggested variables for regulating-profits fraudulent financial statements.

Keywords

accounting; fraud; profitability; radial basis function networks; support vector machines; Chinese listed companies; Lib-SVM detection model; RBF kernel; financial statement fraud; linear kernel; logistic regression model; prediction accuracy; regulating- profits; Accuracy; Companies; Kernel; Logistics; Prediction algorithms; Predictive models; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

E-Product E-Service and E-Entertainment (ICEEE), 2010 International Conference on

Conference_Location

Henan

Print_ISBN

978-1-4244-7159-1

Type

conf

DOI

10.1109/ICEEE.2010.5660371

Filename

5660371