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

Banking Passivity And Regulatory Failure In Emerging Markets: Theory And Evidence From The Czech Republic.

We present a model of bank passivity and regulatory failure. Banks with low equity positions have more incentives to be passive in liquidating bad loans. We show that they tend to hide distress from regulatory authorities and are ready to offer a higher rate of interest in order to attract deposits compared to banks that are not in distress. Therefore, higher deposit rates may act as an early warning signal of bank failure. We provide empirical evidence that the balance sheet information collected by the Czech National Bank is not a better predictor of bank failure than higher deposit rates. This confirms the importance of asymmetric information between banks and the regulator and suggests the usefulness of looking at deposit rate differentials as early signals of distress in emerging market economies where banks' equity positions are often low.http://deepblue.lib.umich.edu/bitstream/2027.42/39808/3/wp424.pd

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

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%)

A Review of the Literature on Early Warning Systems for Banking Crises

This paper presents a review of alternative methodologies for early detection of banking distress. The methodologies proposed are aimed to the early identification of financial distress for countries without an important recent history of bank failure, but facing an unstable international environment. We evaluate several indicators and methodologies to measure financial distress such as qualitative indicators, the signal extraction approach, limited dependent estimation and finally duration models.

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Determinants of the stockholder reactions to convertible debt offering announcements: an analysis of the Western European market.

This paper examines the determinants of the stockholder reactions to convertible debt announcements made by Western European companies. We simultaneously test the impact of issuer characteristics, security design features, the stated uses of proceeds and the aggregate convertible debt issue volume. Our evidence suggests that the announcement returns are positively influenced by the maturity and conversion premium, and negatively influenced by the Eurobond feature, the level of post-conversion equity dilution and the aggregate convertible debt volume. We also document significant interaction effects between the issuer characteristics, the convertible debt design and the convertible debt market condition. First, we find that hot market convertibles are structured to be more 'debt-like' than non-hot market convertibles. Second, we show that the influence of the issuer characteristics depends on the convertible debt design: equity-like convertibles are perceived as instruments able to reduce adverse selection and financial distress costs, whereas debt-like convertibles are perceived as predominantly straight debt. Lastly, we demonstrate that issuer and security characteristics have much more power for explaining the investor reactions during non-hot markets than during hot markets.Determinants; Market; Companies; Characteristics; Security design; Design; Premium; Effects; Selection; Costs; Markets;

Is there a regulatory trade-off between stability and performance? Evidence from italian banks.

Disentangling the direct causal effect that sanctions exert on bank performance from the indirect through default risk, we show that a trade-off exists for regulators between banks’ performance and stability in Italy. Two key findings provide evidence for the nontriviality of the return-risk nexus: (i) banks’ liquidations are concentrated at the lower-end of the profitability distribution, resulting in (attrition) biased estimates; (ii) the drop-out is informative since it depends on the unobserved measurements of profitability. Despite this evidence, while returns are affected by sanctions and regulatory requirements, default risk is not. However, looking at growth of gross loans, enforcement actions reduce default risk though at a cost of a significant fall in lending, creating a regulatory tradeoff. In fact, through loans’ growth, we account for the key dynamics of intermediaries’ soundness, namely higher profits and less non-performing loans