Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical–statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction

Financial crises and bank failures: a review of prediction methods

In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises

Financial crises and bank failures: a review of prediction methods

In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets

Predicting failure in the commercial banking industry

The ability to predict bank failure has become much more important since the mortgage foreclosure crisis began in 2007. The model proposed in this study uses proxies for the regulatory standards embodied in the so-called CAMELS rating system, as well as several local or national economic variables to produce a model that is robust enough to forecast bank failure for the entire commercial bank industry in the United States. This model is able to predict failure (survival) accurately for commercial banks during both the Savings and Loan crisis and the mortgage foreclosure crisis. Other important results include the insignificance of several factors proposed in the literature, including total assets, real price of energy, currency ratio and the interest rate spread.bank failure; banking crises; CAMELS ratings

QUANTIFYING THE VALUE OF MODELS AND DATA: A COMPARISON OF THE PERFORMANCE OF REGRESSION AND NEURAL NETS WHEN DATA QUALITY VARIES

Under circumstances where data quality may vary, knowledge about the potential performance of alternate predictive models can enable a decision maker to design an information system whose value is optimized in two ways. The decision maker can select a model which is least sensitive to predictive degradation in the range of observed data quality variation. And, once the "right" model has been selected, the decision maker can select the appropriate level of data quality in view of the costs of acquiring it. This paper examines a real-world example from the field of finance -- prepayments in mortgage-backed securities (MBS) portfolio management -- to illustrate a methodology that enables such evaluations to be made for two modeling alternative: regression analysis and neural network analysis. The methodology indicates that with "perfect data," the neural network approach outperforms regression in terms of predictive accuracy and utility in a prepayment risk management forecasting system (RMFS). Further, the performance of the neural network model is more robust under conditions of data quality degradation.Information Systems Working Papers Serie

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000–2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.This work has partially been supported by the Mexican Science and Technology Council (CONACYT-Mexico) through a Postdoctoral Fellowship [223351], the Spanish Ministry of Economy under grant TIN2013-46522-P and the Generalitat Valenciana under grant PROMETEOII/2014/062