TY - GEN
T1 - Logistic regression for detecting fraudulent financial statement of listed companies in China
AU - Yue, Dianmin
AU - Wu, Xiaodan
AU - Shen, Nana
AU - Chu, Chao Hsien
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77949302976&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77949302976&partnerID=8YFLogxK
U2 - 10.1109/AICI.2009.421
DO - 10.1109/AICI.2009.421
M3 - Conference contribution
AN - SCOPUS:77949302976
SN - 9780769538167
T3 - 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
SP - 104
EP - 108
BT - 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
T2 - 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
Y2 - 7 November 2009 through 8 November 2009
ER -