Rough sets bankruptcy prediction models versus auditor signalling rates

Abstract

Both international and US auditing standards require auditors to evaluate the risk of bankruptcy when planning an audit and to modify their audit report if the bankruptcy risk remains high at the conclusion of the audit. Bankruptcy prediction is a problematic issue for auditors as the development of a cause-effect relationship between attributes that may cause or be related to bankruptcy and the actual occurrence of bankruptcy is difficult. Recent research indicates that auditors only signal bankruptcy in about 50% of the cases where companies subsequently declare bankruptcy. Rough sets theory is a new approach for dealing with the problem of apparent indiscernibility between objects in a set that has had a reported bankruptcy prediction accuracy ranging from 76% to 88% in two recent studies. These accuracy levels appear to be superior to auditor signalling rates, however, the two prior rough sets studies made no direct comparisons to auditor signalling rates and either employed small sample sizes or non-current data. This study advances research in this area by comparing rough set prediction capability with actual auditor signalling rates for a large sample of United States companies from the 1991 to 1997 time period. Prior bankruptcy prediction research was carefully reviewed to identify 11 possible predictive factors which had both significant theoretical support and were present in multiple studies. These factors were expressed as variables and data for 11 variables was then obtained for 146 bankrupt United States public companies during the years 1991-1997. This sample was then matched in terms of size and industry to 145 non-bankrupt companies from the same time period. The overall sample of 291 companies was divided into development and validation subsamples. Rough sets theory was then used to develop two different bankruptcy prediction models, each containing four variables from the 11 possible predictive variables. The rough sets theory based models achieved 61% and 68% classification accuracy on the validation sample using a progressive classification procedure involving three classification strategies. By comparison, auditors directly signalled going concern problems via opinion modifications for only 54% of the bankrupt companies. However, the auditor signalling rate for bankrupt companies increased to 66% when other opinion modifications related to going concern issues were included. In contrast with prior rough sets theory research which suggested that rough sets theory offered significant bankruptcy predictive improvements for auditors, the rough sets models developed in this research did not provide any significant comparative advantage with regard to prediction accuracy over the actual auditors' methodologies. The current research results should be fairly robust since this rough sets theory based research employed (1) a comparison of the rough sets model results to actual auditor decisions for the same companies, (2) recent data, (3) a relatively large sample size, (4) real world bankruptcy|non-b...

Using Bayesian networks for bankruptcy prediction: Some methodological issues

“…Even auditors, who have good knowledge of firms' situations, often fail to make an accurate judgment on firms' going-concern conditions (e.g., Hopwood et al 1994;McKee 1998McKee , 2003. Therefore, bankruptcy prediction models have become important decision aids for organizations' stakeholders, including auditors, creditors, and stockholders.…”

Bankruptcy Prediction Using Bayesian, Hazard, Mixed Logit and Rough Bayesian Models: A Comparative Analysis

“…An effective prediction in time is valued priceless for business in order to evaluate risks or prevent bankruptcy (Altman et al, 1977;Altman, 1993). A fair amount of research has therefore focused on bankruptcy prediction (Agarwal et al, 2008;Altman, 1968Altman, , 1993Altman, , 2007Altman et al, 1977;Altman et al, 1994;Beaver et al, 2005;Begley et al, 1996;Chava et al, 2004;Grice et al, 2001;Hensher et al, 2007;Hillegeist et al, 2004;Hsieh, 1993;Jones, 1987;Katz et al, 1985;McKee, 2003;Mensah, 1984;Michael et al, 1999;Ohlson, 1980;Ravi et al, 2007;Robertson, et al, 1991;Sarkar et al, 2001;Shumway, 2001;Sun et al, 2007;Tam, 1991;Weiss et al, 2004;West, 1985;Wilson et al, 1994;Zavgren, 1983;Zmijewski, 1984). There may be early warning signs of impending financial distress and this would allow the manager to act in a pre-emptive manner to mitigate the situation from worsening.…”

“…Auditors often fail to make accurate judgments on organizations' going concern conditions, notwithstanding their knowledge of organization (Hopwood et al, 1994;McKee, 2003). An appropriate choice of a bankruptcy model can help auditor in recognizing his disclaimer or qualification as to going concern nature of business (Altman et al, 1974).…”

Default Prediction of Spanish Companies. A Logistic Analysis

“…Therefore, we will comment on it later. Within the artificial intelligence techniques we can count neuronal networks (Ravi and Pramodh, 2008), decision trees (Korol, 2013), rough sets (Tay and Shen, 2002;McKee, 2003), data envelopment analyses (Cielen et al, 2004), support vector machines (Kim and Sohn, 2010) and genetic algorithms (Etemadi et al, 2009), and goal programming (García et al 2013) are the most common ones and some recent works.…”