Multilayer feedforward networks are universal approximators
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Forecasting Economic Data with Neural Networks
Computational Economics
Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering
International Journal of Intelligent Systems in Accounting and Finance Management
A Framework For State Transitions On The Self-Organizing Map: Some Temporal Financial Applications
International Journal of Intelligent Systems in Accounting and Finance Management
Multidimensional Distance-To-Collapse Point And Sovereign Default Prediction
International Journal of Intelligent Systems in Accounting and Finance Management
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We create a neuro-genetic (NG) model for predicting currency crises by using a genetic algorithm for specifying (1) the combination of inputs, (2) the network configuration and (3) the training parameters for a back-propagation artificial neural network (ANN). The performance of the NG model is evaluated by comparing it with standalone probit and ANN models in terms of utility for a policy decision-maker. We show that the NG model provides better in-sample and out-of-sample performance, as well as provides an automatic and more objective calibration of a predictive ANN model. We show that using a genetic algorithm for finding an optimal model specification for an ANN is not only less laborious for the analyst, but also more accurate in terms of classifying in-sample and predicting out-of-sample crises. For a sufficiently parsimonious, but still nonlinear, model for generalized processing of out-of-sample data, the creation and evaluation of models is performed objectively using only in-sample information as well as an early stopping procedure. Copyright © 2011 John Wiley & Sons, Ltd.