The best-practices execution of PCAOB audits requires the use of Analytical Procedures at the Planning and the Substantive Phases. This often finds the auditor using the standard OLS two-parameter linear regression forecasting model [OLSR] to project account-values from the Planning Phase to balances expected at Year-End so as to effect a variance analysis at the Substantive Phase. This is the point of departure of our study. We examine the practical effect of using the OLSR model in a time-series context of the audit. Specifically, this research report provides information on the use of the OLSR model as the model of choice in the audit context compared to the ARIMA(0,2,2)/Holt model which is usually the standard choice for an exponential smoothing model in the presence of autocorrelation of data in the time-stream; autocorrelation is the usual case for longitudinal series taken in the audit. Results: We find that there are reasons to condition the selection of the forecasting model in the Analytical Procedures context based upon autocorrelation in the data-stream. When the time-stream of data exhibits autocorrelation the OLSR model fails in a statistically significant manner to capture the next or one-period ahead client value at the same rate as does the ARIMA/Holt model. This then has implications for the False Negative Investigation Error.Keywords: big-data, Holt, forecasting confidence intervals Forecasting as the Principal Platform in Forming an Analytical Procedures Investigation Projection ContextThe Public Company Accounting Oversight Board [PCAOB] Requires the Use of Analytical Procedures [AP] in the Planning and the Substantive or Completion phases of certification audits. As noted by Arens, Elder, Beasley & Hogan (2015, p.193 This linkage between the Planning and the Substantive phase all but proscribes that there is a projection at the Planning stage, typically formed analytically using previously reported and certified past data and forecasted as an expectation for the client's Year-End value. The modelling form used to generate this projection is very often the standard linear regression model. In fact, the AICPA (2012) initiative: The Clarity Project dealing with Analytical Procedures presents a carefully detailed study called the On the Go Stores case analysis where the forecasting technique used is the standard regression model.After the forecasting projection is made the auditor may examine the difference between the AP forecasting expectation, Applied Finance and Accounting Vol. 4, No. 1; 2018 74 E, and the actual client's account value, A. This is usually referred to as the disposition phase of the AP protocol where if the magnitude of the directional difference between E & A is relatively large, then the auditor is required to consider the meaning of this difference, note in the working papers the likely reason(s), and finally to justify the decision whether or not to further investigate the account.Typically in our experience and that of our colleagues in the audit worl...
We report on a consultation addressing the re-configuration of a Standards Cost Accounting System of a major MNC. We identified two fundamental theoretical issues pertinent to this re-configuration: Their Standards Cost Accounting [SCA] System was (1) not adaptive within their control time frame, and (2) the holistic systemic protocols espoused by W. Edwards Deming were not used to condition the decision-making framework addressing control. We developed an adaptive Decision Support System [SCA:DSS] that offered the following integrated systemic features: (i) The SCA:DSS is parametrized using the Marketing/Sales sub-budget as approved by corporate-level management and (ii) is used to set the control standards for direct Materials & Labor costs and ABC related allocations, (iii) A detailed interactive profiling of production activity is produced at a time when adaptive corrective actions would still be reasonably possible, and (iv) Adaptive: Best, Stasis and Corrective Action Cases regarding the effect of these corrective actions on the contribution margin are displayed. However, even given the adaptive design features and the explicit designs to effect holistic integration over the pilot division and the central headquarters of the firm, the SCA: DSS failed to be implemented. We offer valuable insights into this failure-to-launch that may be indispensable in effecting a synergetic environment where adaptive holistic cost control may be realized. In this paper all of the technical functionalities of the SCA: DSS are detailed and a working illustration is provided. The SCA: DSS is offered as a free download without restrictions to its use.
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