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Research On The Warning Model Of Auditing Collusion

Posted on:2015-08-23Degree:MasterType:Thesis
Country:ChinaCandidate:P J NieFull Text:PDF
GTID:2309330467955219Subject:Accounting
Abstract/Summary:PDF Full Text Request
At present, China is in the stage of deepening the reform of economic system. Thecapital market is developing rapidly, but its development is still restricted by a series offactors of insecurity. Such as the financial report fraud of listed corporation. Theexisting researches have focused on the identification of fraud mainly, and have paidlittle attention to the financial reporting fraud caused by listed corporation and theauditor (i.e. audit collusion). Auditing collusion is not only a serious threat to the orderof the socialist economy with Chinese characteristics and social integrity, but alsoseriously has damaged the interests of the vast number of stakeholders. Because of thesubjectivity of the auditing collusion, it’s very difficult to be discovered. Therefore,this paper tries to establish the warning model of auditing collusion and make acontribution to discovering audit collusion.This paper combines the method of standardized research and empirical research.Firstly the paper explains the theory of basic research and makes a overview of auditconspiracy. Secondly, it analyzes the audit collusion characteristic indexes. Thirdly,according to the notice of administrative penalty in the2003-2012year issued by theCommission, it selects32collusion samples and chooses96non-collusion samples inthe industry whose asset scales are similar to the collusion samples. Fourthly, it screensout12indexes by the normal distribution test, two groups of independent samples Ttest, two groups of independent samples nonparametric tests and principal componentanalysis. Finally, it builds up the warning model of audit collusion by Logistic modelanalysis after extracting12principal component factors.
Keywords/Search Tags:Audit Conspiracy, Auditor Characteristics, Positive Audit Opinion Needs, Logistic Regression
PDF Full Text Request
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