Improving bankruptcy prediction in micro-entities by using nonlinear effects and non-financial variables

The use of non-parametric methodologies, the introduction of non-financial variables, and the development of models geared towards the homogeneous characteristics of corporate sub-populations have recently experienced a surge of interest in the bankruptcy literature. However, no research on default prediction has yet focused on micro-entities (MEs), despite such firms’ importance in the global economy. This paper builds the first bankruptcy model especially designed for MEs by using a wide set of accounts from 1999 to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs outperform the traditional logistic regression (LR) models. In addition, we also report that, thanks to the introduction of non-financial predictors related to age, the delay in filing accounts, legal action by creditors to recover unpaid debts, and the ownership features of the company, the improvement with respect to the use of solely financial information is 3.6%, which is even higher than the improvement that involves the use of the best ANN (2.6%)

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Futures Studies in the Interactive Society

This book consists of papers which were prepared within the framework of the research project (No. T 048539) entitled Futures Studies in the Interactive Society (project leader: Éva Hideg) and funded by the Hungarian Scientific Research Fund (OTKA) between 2005 and 2009. Some discuss the theoretical and methodological questions of futures studies and foresight; others present new approaches to or procedures of certain questions which are very important and topical from the perspective of forecast and foresight practice. Each study was conducted in pursuit of improvement in futures fields

The Default Risk of Firms Examined with Smooth Support Vector Machines

In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Furthermore we showthat oversampling can be employed to gear the tradeoff between error types. Finally, we illustrate graphically how different variants of SSVM can be used jointly to support the decision task of loan officers.Insolvency Prognosis, SVMs, Statistical Learning Theory, Non-parametric Classification

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

The Default Risk of Firms Examined with Smooth Support Vector Machines

In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitabil- ity of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample in°uence the precision of prediction. Furthermore we show that oversampling can be employed to gear the tradeo® between error types. Finally, we illustrate graphically how di®erent variants of SSVM can be used jointly to support the decision task of loan o±cers.Insolvency Prognosis, SVMs, Statistical Learning Theory, Non-parametric Classification models, local time-homogeneity

Are Russian commercial courts biased? Evidence from a Bankruptcy Law Transplant

We study the nature of judicial bias in bankruptcy proceedings following the enactment of the 1998 bankruptcy law in Russia. The two main findings are as follows. First, regional political characteristics affected judicial decisions about the number and types of bankruptcy proceedings initiated after the law took effect. Controlling for indicators of firms' insolvency and the quality of the regional judiciary, reorganization procedures were significantly more frequent in regions with politically popular governors and governors who had hostile relations with the federal center. Poor judicial quality was also associated with higher incidence of reorganizations. Second, the quality of the regional judiciary affected performance of firms under the reorganization procedure: in regions with low quality judges, firms that were reorganized according to the 1998 law had significantly lower growth in sales, labor productivity, and product variety compared to firms not subject to bankruptcy proceedings. In contrast, in regions with high quality judges, firms in reorganization outperformed firms not in bankruptcy proceedings. This effect of judicial quality on the performance of reorganized firms was stronger when governors were politically popular. These findings are consistent with the view that politically strong governors subverted enforcement of the 1998 bankruptcy law. Journal of Comparative Economics35 (2) (2007) 254-277

Modeling Bankruptcy Prediction for Non-Financial Firms: The Case of Pakistan

This paper aims to identify the financial ratios that are most significant in bankruptcy prediction for the non-financial sector of Pakistan based on a sample of companies which became bankrupt over the 1996-2006 period. Twenty four financial ratios covering four important financial attributes namely profitability, liquidity, leverage, and turnover ratios) were examined for a five-year period prior bankruptcy. The discriminant analysis produced a parsimonious model of three variables viz. sales to total assets, EBIT to current liabilities, and cash flow ratio. Our estimates provide evidence that the firms having Z value below zero fall into the “bankrupt” whereas the firms with Z value above zero fall into the “non-bankrupt” category. The model achieved 76.9% prediction accuracy when it is applied to forecast bankruptcies on the underlying sample.Bankruptcy; Z-Score; Non-Financial Firms; Financial Ratios; Pakistan

Bankruptcy Policy Reform and Total Factor Productivity Dynamics in Korea

Using the firm level panel data, obtained from the period between during , this study shows that the failing firms, accepted in the court-administered rehabilitation procedures after the post-crisis bankruptcy reform in Korea, had experienced less persistent problems in the pre-bankruptcy Total-Factor-Productivity (TFP) performances than those before the reform. The most crucial element of the post-crisis reform in the post-crisis court-administered bankruptcy system is the implementation of an economic efficiency criterion, whereas the pre-reform system benefited failing firms deemed as having high social value and prospects for rehabilitation. The new system removes the possibilities for interested parties to oppose the exit of the firms without economic values. Then, to get an idea of how the bankruptcy policy reform would affect the performance of aggregate TFP, we assess the role of the creative destruction process of entry and exit in total factor productivity growth utilizing plant level panel data in the Korean manufacturing sector during the 1990-98 period. For this purpose, we document the plant entry and exit rates, examine the dynamic relationship between plant turnovers and plant productivity, and quantify the contribution from entry and exit to productivity growth. We conclude that, for sustained total factor productivity growth, it is important to establish policy or institutional environment where efficient businesses succeed and inefficient businesses fail.