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

Content Provider	IEEE Xplore Digital Library
Author	Li Xiuzhi Ying Shuangshuang
Copyright Year	2010
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.
Starting Page	1
Ending Page	4
File Size	114699
Page Count	4
File Format	PDF
ISBN	9781424471591
e-ISBN	9781424471614
DOI	10.1109/ICEEE.2010.5660371
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy Companies Predictive models Prediction algorithms Kernel Logistics
Content Type	Text
Resource Type	Article

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