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Neuro-Genetic Predictions of Currency Crises

Peter Sarlin, Dorina Marghescu, Neuro-Genetic Predictions of Currency Crises. Intelligent Systems in Accounting, Finance and Management 18(4), 145–160, 2011.

Abstract:

In this paper, we create a neuro-genetic (NG) model for predicting currency crises by using a genetic algorithm for specifying the (1) combination of inputs, (2) network configuration and (3) training parameters for a back-propagation artificial neural network (ANN). The performance of the NG model is evaluated by comparing it with stand-alone 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 decreases the expertise and labor needed for and uncertainty caused by manual 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 non-linear, 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.

BibTeX entry:

@ARTICLE{jSaMa11b,
  title = {Neuro-Genetic Predictions of Currency Crises},
  author = {Sarlin, Peter and Marghescu, Dorina},
  journal = {Intelligent Systems in Accounting, Finance and Management},
  volume = {18},
  number = {4},
  pages = {145–160},
  year = {2011},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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