Abstract.A strategic internal auditing model is developed to analyze the use of discovery sampling to deter or detect abstraction of assets by an auditee. The analysis develops a game in which the auditee chooses the fraud level and an auditor chooses HB effort level (sample size). The auditee seeks to maximize expected successfui fraud net of a fixed sanction for detected fraud. The auditor seeks to minimize expected costs from sampling and fraud losses. Both simultaneous play aad commitment versions of the game are analyzed. For each version, pure strategies are optimal. In comparison with simultaneous play, the commitment version equilibriam results in greater audit effort and less fraud by the aaditee. Coraparative statics showing effects of sanction level and recovery rates are derived. For simultaneous play, optimal monitoring effort decreases with the sanctioji level and increases with the recovery rate; for the commitment version, these effects are reversed. The analysis demonstrates the sensitivity of results to the audit effort commuaicatioB arrangement and to the specific audit objective of fraud detectioB.
Abstract. This paper reports the results of an experiment examining the framing bias and a potential debiasing technique. Practicing auditors formed a judgment about a hypothetical client's inventory internal control system to determine the amount of related substantive testing. Auditors from two Big Six firms were randomly assigned to one of four treatments in a fully crossed 2 times 2 between‐subjects design. The initial description of the internal control system was identical for all treatments, as were the items of additional evidence about the system. Auditors either judged the risk of the control system or the strength of the control system. The risk‐strength frames were crossed with two levels of debiasing technique: “none” or “evidence rating.” Results indicate that without debiasing, significant framing effects were present, but that evidence rating significantly mitigated the framing effect. In this auditing context, the framing bias appears to be easily induced, but is not robust. Although the profession should be aware of this potential problem, effective remedial or proactive steps can be easily implemented and may naturally occur in current practices.
This paper presents a minimum-cost methodology for determining a statistical Sampling plan in substantive audit tests. In this model, the auditor specifies @, the risk of accepting an account balance as correct when it is not, according to audit evidence requirements. Using @ as a constraint, the auditor then selects a sampling plan to optimize the trade-off between sampling costs and the costs of follow-up audit procedures. Tables to aid in this process and an illustration are provided. Subject A r m : Auditing and Statistid Techniques. INTRODUCTlONThe auditor uses statistical tests of account balances to decide whether to accept or reject a reported balance or book value. In practice, the statistical parameters for selection of a sample are chosen based on one of several methods for judgmental risk evaluation. Some researchers have suggested alternative decision theory approaches in which the auditor's objective is to minimize the expected total costs of sampling and erroneous decisions. In this paper, we present a constrained optimization approach to the determination of sample parameters.Using this approach, the auditor specifies 0, the conditional probability that an account balance will be accepted as correct when it is not, based only on an appraisal of audit evidence requirements independent of cost. Given this constraint, a method is developed for determining other sample parameters that minimize total expected costs in the audit sampling process. FORMULATION OF THE PROBLEMElliott and Rogers [3] have characterized the audit sampling problem as p = actual account balance per item (unknown), pb = reported (book) account balance per item (known), and m = per-item amount considered to be material (specified by auditor). The auditor's inference problem was formulated as a test of the hypothesis: Ho: p = pb vs. the alternative Ha: p=pb-m or p = p b + m . follows. Let 702
Abstract. The stochastic demand cost-volume-profit (CVP) modei has recently received coBsiderable attention. For this model, management must determine optimal production prior tc knowing the actual demand, a stochastic variable with known distribution. Managemeot must choose the production quantity to balance prospects for sales revenue against risks of losses from shortages and from unsold items. This paper develops an expected retam on investment criterion model for determining the optimal production quantity. Formulas and solution methods applicable to general demand distributions are obtained. A special solution technique for normally distributed demand is presented. The resulting choice criterion offers the advantages inherent in return rate methods. !n addition, compared to a profit maximization approach, the expected rate of return on investment criterion is more widely applicable.Resume. Le modele de demande stochastique cout-volume-profit (CVP). a recemment regu considerablement d'atteotion. Avec ce modele, la gestion doit determiner la production optimale avant de connaitre la demande actuelle. une variable stochastique ayant une distribution connue. La gestion doit choisir la quantite a produire afin d'equilibrer les perspectives de ventes et !es risques de pertes resultant des penuries et des unites non vendues., Cet article developpe un modele base sur le rendement espere du capital investi pour determifler la quantite optimale a produire. On obtient des formules et des methodes pouvant s'appliquer k des distributions de demande generale. Une technique de solution particuliere pour une demande distribuee selon une loi normale est presentee. Ce modele offre ies avantages inherents aux methodes du taux de rendement. De plus, comparativement a i'approche de la maximisation du profit, !e critere du taux de rendement espere du capital investi, est applicable dans beaucoup plus de situations.
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