NEURO-GENETIC PREDICTIONS OF CURRENCY CRISES.

SUMMARY 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. [ABSTRACT FROM AUTHOR]