Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Forecasting with neural networks
Information and Management
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Predicting Japanese corporate bankruptcy in terms of financial data using neural network
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
The nature of statistical learning theory
The nature of statistical learning theory
Self organizing neural networks for financial diagnosis
Decision Support Systems
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
An improved neural network for manufacturing cell formation
Decision Support Systems
Mapping of SOM and LVQ algorithms on a tree shape parallel computer system
Parallel Computing
Using Feature Construction to Improve the Performance of Neural Networks
Management Science
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Application of support vector machines to corporate credit rating prediction
Expert Systems with Applications: An International Journal
An application of one-class support vector machines in content-based image retrieval
Expert Systems with Applications: An International Journal
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Financial distress prediction based on serial combination of multiple classifiers
Expert Systems with Applications: An International Journal
Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A stable credit rating model based on learning vector quantization
Intelligent Data Analysis
A genetic algorithm-based approach to cost-sensitive bankruptcy prediction
Expert Systems with Applications: An International Journal
Bankruptcy trajectory analysis on french companies using self-organizing map
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy
Expert Systems with Applications: An International Journal
Enhanced default risk models with SVM+
Expert Systems with Applications: An International Journal
PB-ADVISOR: A private banking multi-investment portfolio advisor
Information Sciences: an International Journal
Clustering and visualization of bankruptcy trajectory using self-organizing map
Expert Systems with Applications: An International Journal
A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures
Computational Economics
Financial performance analysis of European banks using a fuzzified Self-Organizing Map
International Journal of Knowledge-based and Intelligent Engineering Systems
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
| Hi-index | 12.06 |
Bank failures threaten the economic system as a whole. Therefore, predicting bank financial failures is crucial to prevent and/or lessen the incoming negative effects on the economic system. This is originally a classification problem to categorize banks as healthy or non-healthy ones. This study aims to apply various neural network techniques, support vector machines and multivariate statistical methods to the bank failure prediction problem in a Turkish case, and to present a comprehensive computational comparison of the classification performances of the techniques tested. Twenty financial ratios with six feature groups including capital adequacy, asset quality, management quality, earnings, liquidity and sensitivity to market risk (CAMELS) are selected as predictor variables in the study. Four different data sets with different characteristics are developed using official financial data to improve the prediction performance. Each data set is also divided into training and validation sets. In the category of neural networks, four different architectures namely multi-layer perceptron, competitive learning, self-organizing map and learning vector quantization are employed. The multivariate statistical methods; multivariate discriminant analysis, k-means cluster analysis and logistic regression analysis are tested. Experimental results are evaluated with respect to the correct accuracy performance of techniques. Results show that multi-layer perceptron and learning vector quantization can be considered as the most successful models in predicting the financial failure of banks.