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Peer-Based Approach for Analytical Procedures

机译:基于对等方法的分析程序

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This study extends the existing research in analytical procedures by allowing for learning from contemporaneous information transfers among peer companies. We introduce an approach for selecting peers for each client and perform tests to examine the contribution of peers' information to the performance of analytical procedures. We find that peer data are imperfect substitutions for contemporaneous firm-specific variables when such variables are not in error. However, we observe that contemporaneous peer specific data are especially beneficial when coordinated errors exist in multiple accounts. We demonstrate that when errors are seeded into two contemporaneous accounts, peer models perform better at detecting errors. We also find that fast-changing companies experience inferior prediction and error detection accuracy, and that larger companies experience more accurate prediction, lower Type Ⅱ errors, and higher Type Ⅰ errors. Additionally, we observe that significant improvements in the performance of analytical procedures are associated with larger clients indicating that auditors of larger companies can potentially benefit more from the use of peer data.
机译:通过允许从同行公司之间同时进行的信息传递中学习,本研究扩展了对分析程序的现有研究。我们介绍了一种为每个客户选择对等方的方法,并进行测试以检查对等方信息对分析程序性能的贡献。我们发现,当对等数据没有错误时,对等数据不能完美替代同期公司特定的变量。但是,我们观察到,当多个帐户中存在协调错误时,同时进行的对等方特定数据特别有用。我们证明,当错误被植入两个同时发生的帐户时,对等模型在检测错误方面表现更好。我们还发现,变化迅速的公司的预测和错误检测准确度较差,大型公司的预测更准确,Ⅱ类错误较低,Ⅰ类错误较高。此外,我们观察到,分析程序性能的显着提高与较大的客户有关,这表明较大的公司的审计师可以潜在地从使用对等数据中受益。

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