Pervaiz Alam

A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms

In this study, two learning paradigms of neural networks, supervised versus unsupervised, are compared using their representative types. The back-propagation (BP) network and the Kohonen self-organizing feature map, selected as the representative type for supervised and unsupervised neural networks, respectively, are compared in terms of prediction accuracy in the area of bankruptcy prediction. Discriminant analysis and logistic regression are also performed to provide performance benchmarks. The findings suggest that the BP network is a better choice when a target vector is available.

The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study☆

Abstract In this paper, we present experimental results of fuzzy clustering and two self-organizing neural networks used as classification tools for identifying potentially failing banks. We first describe the distinctive characteristics of fuzzy clustering algorithm, which provides probability of the likelihood of bank failure. We then perform the comparison between the results of the closest hard partitioning of fuzzy clustering and of two self-organizing neural networks and present our results as the ranking structure of relative bankruptcy likelihood. Our findings indicate that both the fuzzy clustering and self-organizing neural networks are promising classification tools for identifying potentially failing banks.

Information Technology Capability: Firm Valuation, Earnings Uncertainty, and Forecast Accuracy

Prior literature argues that information technology (IT) capability, an organizations ability to effectively use IT‐based resources in combination with other organizational resources, can create unique and sustainable competitive advantages and thus intangible assets for a company. However, current accounting rules do not allow the capitalization of IT‐enabled intangible assets. We hypothesize that IT‐enabled intangible assets are value‐relevant and provide incremental explanatory power for firm valuation beyond traditional accounting information. IT capability is inherently risky as it is subject to implementation challenges, technological complexity, and innovative integration of IT investments with other organizational resources. Thus, we argue that IT capability is positively associated with future earnings uncertainty and decreased analyst forecast accuracy. Using InformationWeek 500 ranking index as a proxy for IT capability, we find evidence supporting these hypotheses. This study contributes to t...

The design and validation of a hybrid information system for the auditor's going concern decision

Decision making in a semistructured environment often involves the use of quantitative, structured analysis along with the qualitative judgment of an expert. Decision support systems and expert systems are often developed to assist in this judgment process. The hybrid information system described in this paper combines a statistical model with a rule-based expert system in order to integrate the quantitative and qualitative aspects of decision making. The GC Advisor hybrid system is designed for use by auditors to assess the ability of the client firm to continue as a going concern. The guidelines for expert system validation given in previous literature are then applied to the validation of GC Advisor.

An Analysis of Fuzzy Clustering and a Hybrid Model for the Auditor's Going Concern Assessment*

This study provides a description and testing of fuzzy clustering and a hybrid model that can support the decision an auditor makes when completing the going concern evaluation. Fuzzy clustering is based on fuzzy logic, and the hybrid system is designed to address the going concern decision through the combined use of a statistical model and an expert system. These models have the capability of identifying categories of firms with particular characteristics that may indicate whether or not the audit report of the firms requires a going concern modification. A prediction of whether or not a firm may go bankrupt is included as one of the components of the going concern decision. As a result, if a firm is placed in a particular bankrupt category by a decision model, it may help in the determination of the auditors opinion regarding the continuity of the business.

Decision-making capabilities of a hybrid system applied to the auditor's going-concern assessment

The purpose of this study is to evaluate a hybrid system as a decision support model to assist with the auditors going-concern assessment. The going-concern assessment is often an unstructured decision that involves the use of both qualitative and quantitative information. An expert system that predicts the going-concern decision has been developed in consultation with partners at three of the Big Five accounting firms. This system is combined with a statistical model that predicts bankruptcy, as a component of the auditors decision, to form a hybrid system. The hybrid system, because it combines the use of quantitative and qualitative information, has the potential for better prediction accuracy than either the expert system or statistical model predicting separately. In addition, testing of the system provides some insight into the characteristics of firms that experience problems, but do not necessarily receive a going-concern modification. Further investigation into those firms that have problems could reveal factors that may be incorporated into decision support systems for auditors, in order to improve accuracy and reliability of these decision tools.

Auditor lobbying for accounting standards: The case of banks and savings and loan associations

Abstract This study examines auditor lobbying on seven proposed US accounting standards which affect banks and savings and loan associations. Evidence is provided in support of the Watts and Zimmerman (1982, 1986) theory on auditor lobbying. Watts and Zimmerman (WZ) hypothesise that auditor lobbying is a function of the client-manager position and a set of wealth effect variables. These variables may provide an incentive for auditors to disagree with their clients on proposed accounting issues. The WZ model is modified by including an audit risk variable. Results show that the model is statistically significant and that the identified wealth and audit risk effects are significant explanatory variables of auditor lobbying behaviour.

CEO Compensation and Stakeholders' Claims

The traditional view that corporations exist solely to serve the interests of the firms shareholders has given way to a changing view that recognizes the importance of corporate constituents in addition to shareholders. Prior studies demonstrate a significant association between the sensitivity of CEO compensation and firms stock prices. However, the association between CEO compensation and the claim of other primary stakeholders (customers, employees, suppliers) has not been examined. The purpose of this study is to investigate whether the adoption of long-term incentive plans align the interest of the CEO with the interest of the primary stakeholders in the firm. Using the fixed-effect regression, our results indicate a significant association between the change in CEO compensation and the claims of the customers, shareholders, and the employees. We contribute to the literature by demonstrating that the managers are not only accountable to the shareholders but also to primary stakeholders.

R&D expenditures and implied equity risk premiums

This study investigates the relationship between research and development (R&D) expenditures and risk premiums implied in the costs of equity capital. We posit that R&D expenditures represent an information risk factor resulting from both information asymmetry about R&D between investors and managers and low-quality R&D reporting that impairs the coordination between investors and managers with respect to managers’ investment decisions. Our results support our position by showing a positive association between R&D expenditures and implied equity risk premiums. From this research along with prior studies, investors can have better knowledge about the risky nature of R&D expenditures that drive up implied risk premiums and at the same time provide opportunities to earn excess returns in a short to long horizon. Accounting standard setters can benefit from this study’s findings that R&D expenditures represent an off-balance-sheet risk factor and thus warrant reconsidering SFAS No. 2 for potential capitalization of R&D expenditures. Copyright Springer Science+Business Media New York 2014

Disaggregated earnings and the prediction of ROE and stock prices: a case of the banking industry

Purpose – This paper seeks to investigate whether disaggregated bank earnings better predict next period earnings than contemporaneous aggregated earnings. Design/methodology/approach – Fairfield et al.s (1996) regression approach is used for predicting next periods return of equity (ROE) and stock prices using disaggregated earnings data. Findings – The results show that the mean adjusted R-square significantly increases with the progressive disaggregation of earnings. The results also demonstrate that disaggregated components are better able to predict next period earnings and stock prices than aggregated earnings. Research limitations/implications – The findings support the US Financial Accounting Standard Boards contention that disaggregated information may be more useful than aggregated information for investment, credit, and financing decisions. Practical implications – Investors and analysts should use disaggregated income statement information in predicting next period earnings and stock prices for the banking industry. Originality/value – The main contribution of this paper is to demonstrate how fully disaggregated earnings explain ROE, stock prices, and analysts forecast error in the banking industry.