The Error Detection of Structural Analytical Procedures: A Simulation Study.
通过模拟公司财务数据，研究结构性分析程序在预测和错误检测中的表现，发现其优于ARIMA等模型，但不如间接利用经济结构信息的逐步模型，且销售行为模式和经济稳定性显著影响模型性能。
Abstract Given the requirement of SAS No. 56 and the increasing pressures to minimize audit costs, there is a need to develop more sophisticated analytical procedures that can increase the effectiveness and efficiency of an audit. Prior research suggests that structural models including the futuristic concept of an "information dual" may be good for this purpose. This study extends the work of Wheeler and Pany (1990) and Wild (1987) and investigates the prediction and error detection performance of structural analytical procedures using the monthly financial statements of a large number of simulated companies. These companies represent various sales behavior patterns and degrees of economic stability. We develop a generic structural model that explicitly incorporates interdependencies among the accounting numbers and key exogenous variables that drive the economic environment of the company. When compared to the ARIMA, X-11, and Martingale models, our structural model performs better from an overall perspective. However, it does not perform better than the stepwise model which indirectly incorporates information on the structure of an organization's economic activities. The results indicate that the performance of each model, with respect to alpha and beta risks, tends to be a function of the testing approach used. We use both the positive and negative testing approaches. The sales behavior pattern has a significant impact on the performance of each model. All models tend to perform better for companies that have a greater degree of stability in their business and economic activities. In general, our results suggest that auditors can improve the prediction and error detection capability of analytical procedures by using the information inherent in the natural structure of accounting systems which reflect business and economic activities.