This study compares the performance of three artificial neural network (ANN) approaches-backpropagation, categorical learning, and probabilistic neural networkas classification tools to assist and support auditor's judgment about a client's continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassification (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.

This paper investigates the predictive value of tangible long-lived asset impairments for changes in future operating cash flows under U.S. GAAP and IFRS. We find that impairments reported under IFRS are negatively associated with changes in future operating cash flows, whereas those under U.S. GAAP, on average, are not. We investigate whether differences in the predictive value are attributable to differences in recognition or measurement, providing evidence suggesting that impairment recognition under U.S. GAAP is delayed. Evidence also suggests that the value-in-use measurement attribute, allowed under IFRS, does not induce under-impairing as IFRS and U.S. GAAP impairments are similarly related to future impairments. The main result of a negative association under IFRS, but not U.S. GAAP, holds after considering future impairments to control for measurement differences, macro-economic factors, and firm reporting incentives. Further, impairment losses under IFRS are more predictive in high-enforcement countries. JEL Classifications: D78; F02; M16; M41; G38. Data Availability: Data used are available from sources identified in the paper.

No abstract

Purpose The purpose of this paper is to investigate the impact of the public pension governance practices on the public defined benefit pension (DBP) fund performance. Design/methodology/approach To provide a holistic evaluation of public DBP performance, this study first employs the Data Envelopment Analysis (DEA) approach to construct a relative performance measure that simultaneously takes into account the association between investment inputs and performance outputs across DBPs in our sample. A DEA regression model is then constructed to empirically examine the impact of pension governance on public DBP performance. Findings Using 1,544 hand-collected observations in the USA from 2002 to 2013, the findings show that the public DBP plans with a small board, appointed board trustees, and a separate investment council exhibit better performance. Practical implications The effectiveness of pension governance has increasingly drawn public attention, as it affects the performance of the public DBP plans that especially matter to public employees. The empirical findings of this research offer insights into recent calls to reexamine public DBP management practices and to carry out related public pension fund policy reforms. Originality/value The examination of public DBP governance practices in this study enriches the governance literature, particularly research on public pension funds, by using public sector data. Second, by applying the DEA method to evaluate the relative performance of public DBP funds, this study obtains a more comprehensive analysis of the public pension governance.

Purpose The purpose of this paper is to investigate the roles of chief accounting officer (CAO) on the efficiency of auditing process and to empirically examine the association between separate CAO appointment and audit report lag (ARL). Design/methodology/approach This study employs firms listed in the US market from 2004 to 2012. The firm year having a CAO who does not simultaneously take other executive position is specifically identified. Firm years with job titles similar to CAO, such as chief accounting executive, vice president of accounting or corporate accounting executive, are categorized into the CAO group. Findings The presence of a separate CAO significantly reduces ARL. With the appointment of a new auditor, the presence of a separate CAO is associated with lower ARL, suggesting the moderating effect of separate CAOs on the relationship between auditor change and audit delay. Practical implications This study shows the importance of CAO, an executive who is specifically responsible for carrying out accounting functions. The findings suggesting the positive effects of separate CAO on external audit process and the timeliness of information should be of interest to firms, financial reporting users, auditors and regulators. Originality/value While few studies address CAO-related issues, the roles of a CAO are not widely explored and how a separate CAO affects external audit process remains an open question. This study fills this gap and further documents the contribution of separate CAO in external audit work to enrich literature in executive roles and audit efficiency at the same time.