Thomas E. McKee

Genetic programming and rough sets: A hybrid approach to bankruptcy classification

Abstract The high social costs associated with bankruptcy have spurred searches for better theoretical understanding and prediction capability. In this paper, we investigate a hybrid approach to bankruptcy prediction, using a genetic programming algorithm to construct a bankruptcy prediction model with variables from a rough sets model derived in prior research. Both studies used data from 291 US public companies for the period 1991 to 1997. The second stage genetic programming model developed in this research consists of a decision model that is 80% accurate on a validation sample as compared to the original rough sets model which was 67% accurate. Additionally, the genetic programming model reveals relationships between variables that are not apparent in either the rough sets model or prior research. These findings indicate that genetic programming coupled with rough sets theory can be an efficient and effective hybrid modeling approach both for developing a robust bankruptcy prediction model and for offering additional theoretical insights.

Bankruptcy theory development and classification via genetic programming

Abstract Bankruptcy is a highly significant worldwide problem with high social costs. Traditional bankruptcy risk models have been criticized for falling short with respect to bankruptcy theory building due to either modeling assumptions or model complexity. Genetic programming minimizes the amount of a priori structure that is associated with traditional functional forms and statistical selection procedures, but still produces easily understandable and implementable models. Genetic programming was used to analyze 28 potential bankruptcy variables found to be significant in multiple prior research studies, including 10 fraud risk factors. Data was taken from a sample of 422 bankrupt and non-bankrupt Norwegian companies for the period 1993–1998. Six variables were determined to be significant. A genetic programming model was developed for the six variables from an expanded sample of 1136 bankrupt and non-bankrupt Norwegian companies. The model was 81% accurate on a validation sample, slightly better than prior genetic programming research on US public companies, and statistically significantly better than the 77% accuracy of a traditional logit model developed using the same variables and data. The most significant variable in the final model was the prior auditor opinion, thus validating the information value of the auditor’s report. The model provides insight into the complex interaction of bankruptcy related factors, especially the effect of company size. The results suggest that accounting information, including the auditor’s evaluation of it, is more important for larger than smaller firms. It also suggests that for small firms the most important information is liquidity and non-accounting information. The genetic programming model relationships developed in this study also support prior bankruptcy research, including the finding that company size decreases bankruptcy risk when profits are positive. It also confirms that very high profit levels are associated with increased bankruptcy risk even for large companies an association that may be reflecting the potential for management to be “Cooking the Books”.

Developing a bankruptcy prediction model via rough sets theory

The high individual and social costs encountered in corporate bankruptcies make this decision problem very important to parties such as auditors, management, government policy makers, and investors. Bankruptcy is a worldwide problem and the number of bankruptcies can be considered an index of the robustness of individual country economies. The costs associated with this problem have led to special disclosure responsibilities for both management and auditors. Bankruptcy prediction is a problematic issue for all parties associated with corporate reporting since the development of a cause–effect relationship between the many attributes that may cause or be related to bankruptcy and the actual occurrence of bankruptcy is difficult. An approach that has been proposed for dealing with this type of prediction problem is rough sets theory. Rough sets theory involves a calculus of partitions. A rough sets theory based model has the following advantages: (1) the rough sets data analysis process results in the information contained in a large number of cases being reduced to a model containing a generalized description of knowledge, (2) the model is a set of easily understandable decision rules which do not normally need interpretation, (3) each decision rule is supported by a set of real examples, (4) additional information like probabilities in statistics or grade of membership in fuzzy set theory is not required. In keeping with the philosophy of building on prior research, variables identified in prior recursive partitioning research were used to develop a rough sets bankruptcy prediction model. The model was 93% accurate in predicting bankruptcy on a 100-company developmental sample and 88% accurate on the overall separate 100-company holdout sample. This was superior to the original recursive partitioning model which was only 65% accurate on the same data set. The current research findings are also compared, both in terms of predictive results and variables identified, to three prior rough sets empirical bankruptcy prediction studies. The model produced by the current research had a significantly higher prediction accuracy on its validation sample and employed fewer variables. This research significantly extends prior rough sets bankruptcy prediction research by using a larger sample size and data from U.S. public companies. Implications for both bankruptcy prediction and future research are explored. Copyright

Assurance practitioners' and educators' self-perceived IT knowledge level: an empirical assessment

Abstract In September 2001, the International Federation of Accountants (IFAC) Education Committee issued Exposure Draft IEG-1, which states, “Information technology is pervasive in the world of business. Competence with this technology is an imperative for the professional accountant.” This view was seconded by a leading visionary in the assurance profession, who recently stated that “every aspect of the accounting profession is being pervasively affected by advances in information technology” [Audit. J. Pract. Theory 21 (1) 2002]. Clearly, accounting/auditing education needs to incorporate these technology changes to stay relevant. Despite the apparent need for change, leading academicians have recently talked about the possible demise of accounting/auditing education due to a failure keep up with changes in the business world. Accounting/auditing education has been described as outdated, broken, and in need of significant modification (Albrecht WS, Sack RJ. Accounting education: charting the course through a perilous future. Accounting education series. Sarasota, FL: American Accounting Association, vol. 16, 2000). This study focuses on identifying appropriate information technologies (ITs) for auditing professors and audit practitioners and measuring their self-perceptions of knowledge about these technologies. We conducted a literature search that resulted in the identification of 36 critical information technologies. We then surveyed 1000 accounting information systems (AIS) and auditing professors and 1000 audit practitioners to determine their self-reported IT knowledge levels and perceptions about the best places to learn IT skills. The survey also solicited their views about the best place in the educational process for initially learning these technologies, as well as views about educational methodology. Response rates of 31.2% for professors and 24.7% for practitioners were obtained for the survey. After conducting factor analysis, we found a relatively low level of knowledge for e-commerce and advanced technologies and audit automation constructs by both educators and practitioners, but we found a relatively high level of knowledge for office automation and accounting firm office automation constructs. Results also indicate that the educators with more teaching experience, but lower reported IT knowledge levels, tend to teach auditing. Furthermore, we found a potential “learning gap” between educators and practitioners that may occur for 5 of the 36 technologies examined. The results of this research are important for auditing and AIS education because they strongly suggest that more attention needs to be paid to issues such as assigning courses based on knowledge level rather than seniority, technology training and awareness programs for educators and practitioners, and coverage of IT in the university curriculum.

Predicting bankruptcy via induction

Research has consistently shown that auditors disclose going-concern problems for less than 50% of all business failures. As evidenced by widespread litigation, investors and creditors believe that this performance needs to be improved. This paper reports on research which analysed financial data for 60 public companies via an inductive inferencing algorithm. The end result was a simple and theoretically consistent model that was 97% accurate in predicting bankruptcy. Auditors, investors and creditors may find the model useful in improving their bankruptcy prediction capability.

A comparison of Norwegian and United States accounting students' learning style preferences

Individual differences among humans are apparent in many environments and situations. Educators should be concerned about whether such differences are systematic to such an extent that they significantly affect the effectiveness of various pedagogical methods and training approaches. This paper investigates one type of individual difference-learning style-which may vary across cultures and which may be relevant for both accounting education and audit practice. Learning style is defined as an individual orientation to learning, utilizing four basic modes of learning to various degrees. Learning style measures were obtained from Norwegian and United States accounting students with varying experience levels. The learning styles for the US students were compared to data obtained in prior research and were found to be generally consistent with these studies. The Norwegian sample was compared to both the current United States results and prior research and was found to be significantly different from the US lea...

Increase your fraud auditing effectiveness by being unpredictable

Purpose – To identify how auditors can incorporate unpredictability into their audit plan in order to comply with both US and international auditing standards on the prevention and detection of fraud.Design/methodology/approach – Review of auditing standards, fraud cases, and other audit literature.Findings – A cost‐benefit model for evaluating unpredictability and 17 specific ways that auditors can incorporate unpredictability.Practical implications – This paper can be used by practicing auditors to develop ways to increase their compliance with professional standards.Originality/value – The paper fills a void in the literature with respect to how auditors can be unpredictable as required by auditing standards.

Citation “snapshot” of three leading international auditing journals

Purpose - This paper aims to examine the “impact” of three leading international auditing journals via citation analysis. Design/methodology/approach - A Google Scholar citation analysis was conducted for the period 2001-2006 for Findings - javax.xml.bind.JAXBElement@3f9baf3c Originality/value - This is the first paper to examine recent citations from these three journals. The research provides a basis for an author to evaluate potential “impact” from a research submission to these three journals.

Accounting for special purpose entities: The control view versus the primary beneficiary view for consolidation

This article provides an analysis of the economic incentives and financial reporting for Special Purpose Entities (SPEs) over the last four decades. The analysis explains economic factors motivating business use of SPEs and the origins of SPEs in lease accounting and securitization transactions. Related financial reporting standards are identified and discussed, including the historical shift from a traditional control viewpoint to a primary beneficiary viewpoint for financial reporting for consolidation for SPEs (recently renamed Variable Interest Entities (VIEs) in U.S. Financial Accounting Interpretation 46R). The article also includes illustrative journal entries explaining SPE transactions from both the viewpoint of the creating company(s) and the SPE. Actual financial reporting examples and/or journal entries for SPEs created by Bank of America, General Motors Acceptance Corporation, Lucent Technologies and Alza Pharmaceuticals Corporation are also provided.