Review of neural networks for speech recognition
Neural Computation
Forecasting exchange rates using general regression neural networks
Computers and Operations Research - Neural networks in business
Neural Networks Finance and Investment: Using Artificial Intelligence to Improve Real-World Performance
Computers and Operations Research - Special issue: Emerging economics
IEEE Transactions on Neural Networks
Semiparametric ARX neural-network models with an application to forecasting inflation
IEEE Transactions on Neural Networks
A comparison of nonlinear methods for predicting earnings surprises and returns
IEEE Transactions on Neural Networks
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Improving artificial neural networks' performance in seasonal time series forecasting
Information Sciences: an International Journal
Analysis and modeling of multivariate chaotic time series based on neural network
Expert Systems with Applications: An International Journal
The application of Web ATMs in e-payment industry: A case study
Expert Systems with Applications: An International Journal
A hybrid approach for supplier cluster analysis
Computers & Mathematics with Applications
Using artificial neural network models in stock market index prediction
Expert Systems with Applications: An International Journal
Using support vector machine for characteristics prediction of hydraulic valve
International Journal of Computer Applications in Technology
Linear non-Gaussian causal discovery from a composite set of major US macroeconomic factors
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
The performance of neural networks in evaluating and forecasting banking crises have been examined in this paper. An artificial neural network model which works with the banking data belonging to the same date and another artificial neural network model which works with cross sectional banking data have been formed and tested. The optimal topologies of these models have been determined by Taguchi approach which is a design of experiments method. Both models can forecast the values of the output neurons consisting of Non-performing Loans/Total loans, Capital/Assets, Profits/Assets and Equity/Assets ratios by using 25 input neurons consisting of macroeconomic variables, the variables related to the external balanced financial system's structure, and time with very small errors. Consequently, it has been seen that artificial neural networks which are capable of producing successful solutions for semi-structural and non-structural problems, can be used effectively in evaluating and forecasting banking crises.