The error detection of structural analytical review procedures : a simulation study

Abstract

SUMMARY 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 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. Key Words: Analytical procedures, Structural model, Error detection, Simulation, Risk. Data Availability: Contact the authors. INTRODUCTION Auditors use analytical procedures to test the reasonableness of recorded accounting numbers based on the company's history and concurrent economic conditions. According to Statement on Auditing Standards (SAS) No. 56 (AICPA 1988), analytical procedures are required in the planning and overall review stages of the audit and are recommended during substantive testing. Many prior studies have emphasized the significance of analytical procedures in detecting financial statement errors and improving audit effectiveness (Hylas and Ashton 1982; Biggs and Wild 1984; Tabor and Willis 1985). Effective analytical procedures can help auditors identify errors and risk areas that require more thorough tests. As Hylas and Ashton (1982, 764) state: A large proportion of financial statement errors are initially signaled by less rigorous audit procedures such as analytical review.... It appears that increased utilization of less rigorous audit procedures might improve the auditor's effectiveness and/ or efficiency in detecting errors, and also allow a fine tuning of substantive tests of detail. Given the requirement of SAS No. 56 and the increasing pressures to minimize audit costs, the development of more sophisticated and effective analytical procedures is desirable. The objective of this paper is to extend audit research on the effectiveness of analytical procedures by examining the ability of structural expectation models to predict account balances and to detect seeded errors. We test the predictive ability and the error detection performance of a structural model for a large number of simulated companies which represent various sales behavior patterns and degrees of stability in their economic activities. Specifically, we posit that structural models will generate predicted values significantly closer to simulated values and as a result have smaller mean absolute prediction errors than several previously examined analytical procedures. …

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