Predicting going concern opinion with data mining
Introduction
Statement on Auditing Standards (SAS) No. 59 [1] requires that on every audit the auditor evaluates whether substantial doubt exists about the client entity's ability to continue as a going concern. In particular, the auditor has to assess the client's going concern status for a reasonable period of time, not to exceed one year beyond the date of the financial statements being audited. Relevant information with respect to the continuation of an entity as a going concern is generally obtained from the application of auditing procedures that are planned and performed to achieve audit objectives. Examples of conditions and events that cast doubt on the entity's ability to survive include negative financial trends, defaults on loans or similar agreements, and non-financial internal and external matters such as work stoppages or substantial dependence on the success of a particular project. When the identified conditions and events in the aggregate lead to substantial doubt about the continued existence of the entity as a going concern, the auditor should identify and evaluate management's plans to mitigate the effects of these adverse conditions or events. If the auditor believes that there exist management plans that overcome this substantial doubt, a going concern audit report is not required. However, if the auditor decides that substantial doubt exists, the audit report should be modified by adding an explanatory paragraph following the opinion paragraph.
Although the assessment of a company's viability is not the main objective of an audit, bankruptcies without a prior going concern report are often viewed by the public as audit reporting failures [35], [13], [22]. The high frequency of this type of audit reporting failures is indicative of the fact that the auditor's going concern decision is highly complicated and involves a high level of judgment.
The complexity of the going concern decision has prompted the development of numerous models to predict the issuance of a going concern opinion (see, for example, [37], [30], [17], [36], [6]). The focus of these studies has been the development of going concern prediction models, proposing a variety of financial and non-financial variables that might be indicative of the auditor's going concern decision.
Most of these prediction models were developed using regression analysis, a technique which is well suited for investigating the determinants of going concern decision-making but less appropriate for developing user-friendly going concern decision models that can be used in everyday auditing. In this paper, we address this gap in the going concern literature by building a comprehensible rule-based classification model which allows for easy consultation by auditors to assess their client's viability. The classification model developed in this study is particularly useful to auditors to screen potential clients or as a decision aid to identify severely distressed clients that might require further consideration. Moreover, auditors may use this model in the final stages of the audit engagement as a quality control device or as a benchmark to represent auditor judgment under similar circumstances.
Furthermore, we will address the appropriateness of the methodology of recent going concern research. In particular, we will evaluate the performance of various data mining techniques including logistic regression and the rule-based classification technique used in this study. In addition, we will examine empirically potential estimation biases induced by the choice-based sampling methodology used in recent going concern research. We compare estimation results from a “complete data” sample with estimation results from choice-based sampling techniques currently used in going concern research. In sum, we contribute to existing going concern research by (a) developing a practical and user-friendly going concern decision-aid for audit practitioners and (b) critically reviewing the methodology of recent going concern research.
Section snippets
Predicting the going concern opinion
In this section, we provide an overview of some relevant prior studies that have investigated the auditor's going concern judgment. Most of these studies investigated the influence of the quantifiable and non-quantifiable factors identified by SAS No. 34 and SAS No.59 on the issuance of a qualified opinion (e.g. [37], [17], [13], [25], [5], [20]). An overview of related papers is shown in Table 1, where the columns describe the sampling technique and methodology used.
Of the included companies,
Data mining
Over the past decades we have witnessed an explosion of data. Although much information is available in this data, it is hidden in the vast collection of raw data. Data mining entails the overall process of extracting knowledge from this data.
Different types of data mining are discussed in the literature (see a.o. [2]), such as regression, classification and clustering. The task of interest here is classification, which is the task of assigning a data point to a predefined class or group
Conclusions
The relevance and success of data mining for the going concern decision is driven by a number of factors. First of all, much data of previously audited firms is available, a prerequisite for any data mining application. Secondly, the going concern decision is a complex task with widespread consequences to both the company being audited and the auditor, for which decision support systems are more than welcome. This has prompted the development of numerous models to predict the issuance of a
Acknowledgments
We extend our gratitude to the editor and the anonymous reviewers, as their constructive remarks certainly contributed much to the quality of this paper. Further, we would like to thank the Flemish Research Council (FWO, Grant G.0615.05) for the financial support.
David Martens received a PhD in Applied Economic Sciences from the Department of Decision Sciences and Information Management of K.U.Leuven, Belgium, in 2008. He also received a Master's degree in civil engineering at the Computer Science Department from K.U. Leuven, Belgium in 2003; and a Master of Business Administration in 2005 from Reims Management School, France. His research is mainly focused on the development of comprehensible data mining techniques, with main application the building
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David Martens received a PhD in Applied Economic Sciences from the Department of Decision Sciences and Information Management of K.U.Leuven, Belgium, in 2008. He also received a Master's degree in civil engineering at the Computer Science Department from K.U. Leuven, Belgium in 2003; and a Master of Business Administration in 2005 from Reims Management School, France. His research is mainly focused on the development of comprehensible data mining techniques, with main application the building of Basel II-compliant credit scoring systems.
Liesbeth Bruynseels received the M.Sc. and Ph.D. degree in Applied Economic Sciences from the K.U.Leuven (Belgium) in 2001 and 2006, respectively. She is currently working as an assistant professor of accounting at the University of Tilburg (The Netherlands). Her research interests include auditor reporting, auditor judgment and decision making and audit quality.
Bart Baesens received the M.Sc. and Ph.D. degree in Applied Economic Sciences from the K.U.Leuven (Belgium) in 1998 and 2003, respectively. He is currently working as an assistant professor at K.U.Leuven (Belgium) and as a lecturer at the University of Southampton (United Kingdom). His research interests include classification, rule extraction, neural networks, support vector machines, data mining, and credit scoring.
Marleen Willekens is professor of financial accounting and auditing at the K.U.Leuven (Belgium) and the University of Tilburg (the Netherlands). She has earned a PhD from the University of Warwick Business School. Her research is focused on economical aspects of auditing and the information value of financial reporting. Marleen Willekens teaches financial accounting and auditing courses both in graduate and undergraduate programmes.
Jan Vanthienen received the M.Sc. and Ph.D. degree in Applied Economic Sciences from the K.U.Leuven (Belgium). He is professor at the Department of Decision Sciences and Information Management of K.U.Leuven. His main research themes are business rules and information management.