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Detecting Real Activities Manipulation: Beyond Performance Matching
Abacus ( IF 2.060 ) Pub Date : 2021-03-14 , DOI: 10.1111/abac.12221
THOMAS A. GILLIAM 1
Affiliation  

The use of real activities manipulation (RAM) to mislead stakeholders has garnered the focus of earnings management research. A prominent feature of RAM research is its use of estimation models in conjunction with performance matching. The veracity of this research dependents on performance matching to mitigate estimation bias. This paper provides comprehensive tests of the RAM models and their performance-matched counterparts and puts forth a set of model modifications to directly address weaknesses in the models’ specifications. In comparative tests, the performance-matched models leave residual bias, while the modified models remove it. Similarly, in test of power, with simulated RAM, the performance-matched models fail to detect high levels of simulation, for example, 5% of assets, while the modified models demonstrate power. These results, combined with the examination of actual and counterfactual RAM settings, call into question the use of the performance-matched RAM models. On the other hand, the additional tests provide further evidence of the modified models’ accuracy and ability to detect RAM.

中文翻译:

检测真实活动操纵:超越性能匹配

利用真实活动操纵(RAM)误导利益相关者已成为盈余管理研究的焦点。RAM 研究的一个突出特点是它使用估计模型与性能匹配相结合。这项研究的准确性取决于性能匹配以减轻估计偏差。本文对 RAM 模型及其性能匹配的对应物进行了全面测试,并提出了一组模型修改,以直接解决模型规范中的弱点。在比较测试中,性能匹配的模型会留下残余偏差,而修改后的模型会消除它。同样,在使用模拟 RAM 进行功率测试时,性能匹配的模型无法检测到高水平的模拟,例如 5% 的资产,而修改后的模型显示了功率。这些结果,结合对实际和反事实 RAM 设置的检查,对性能匹配的 RAM 模型的使用提出了质疑。另一方面,额外的测试进一步证明了修改后的模型的准确性和检测 RAM 的能力。
更新日期:2021-03-14
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