Logistic regression for detecting fraudulent financial statement of listed companies in China

Dianmin Yue, Xiaodan Wu, Nana Shen, Chao Hsien Chu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

This paper examines published data to develop a model of Logistic Regression for detecting factors associated with Fraudulent Financial Statement (FFS). After an exhaustive exploitation of prior work used financial ratios, 21 ratios are selected as potential predictors of FFS and a series of experiments have been conducted to determine the optimal parameters for Logistic model. Then, we propose an appropriate model for detecting FFS of listed companies in China and compare its predictive ability with other detecting models using a data set of 174 listed companies in China including 87 with FFS and 87 with non-FFS during the period 1993-2007. The results demonstrate that the predictive ability of the model proposed in this paper is higher than other models at about 10% by using the optimal parameters determined and indicate the importance of financial ratios, which could benefit both internal and external auditors, taxation and other state authorities.

Original languageEnglish (US)
Title of host publication2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
Pages104-108
Number of pages5
DOIs
StatePublished - Dec 1 2009
Event2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009 - Shanghai, China
Duration: Nov 7 2009Nov 8 2009

Publication series

Name2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
Volume2

Other

Other2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
CountryChina
CityShanghai
Period11/7/0911/8/09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Software

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