Fraud Triangle Theory and Identification of Financial Fraud From China’s Listed Company
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This paper empirically examines the effectiveness of fraud risk factors proposed by Cressey's fraud triangle theory in order to assess the likelihood of financial fraud in the Chinese stock market. We select potential fraud risk factors which serve as proxies for pressure, opportunity, rationalization, and then test these factors using a Chinese sample of fraud firms and matched non-fraud firms. We identify that three(NP, CR, MOWN) pressure factors, two(PCS, ASM) opportunity factors, and two(AFS, LA) rationalization factors can be applied as significant proxy variables to estimate the likelihood of financial fraud. Especially, our findings suggest that net profit(NP), annual stockholders’ meeting(ASM), audit firm switch(AFS) are negatively related to the likelihood of financial fraud, but level of accurals(LA) is positively related to the likelihood of financial fraud. We also find that our discriminant models have relatively high classification accuracy and provide an important predicting variable in detecting financial fraud.
Abstract
This paper empirically examines the effectiveness of fraud risk factors proposed by Cressey's fraud triangle theory in order to assess the likelihood of financial fraud in the Chinese stock market. We select potential fraud risk factors which serve as proxies for pressure, opportunity, rationalization, and then test these factors using a Chinese sample of fraud firms and matched non-fraud firms. We identify that three(NP, CR, MOWN) pressure factors, two(PCS, ASM) opportunity factors, and two(AFS, LA) rationalization factors can be applied as significant proxy variables to estimate the likelihood of financial fraud. Especially, our findings suggest that net profit(NP), annual stockholders’ meeting(ASM), audit firm switch(AFS) are negatively related to the likelihood of financial fraud, but level of accurals(LA) is positively related to the likelihood of financial fraud. We also find that our discriminant models have relatively high classification accuracy and provide an important predicting variable in detecting financial fraud.
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