NEURO‐GENETIC PREDICTIONS OF CURRENCY CRISES

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.