Review article
COMPARING THE BANK FAILURE PREDICTION PERFORMANCE OF NEURAL NETWORKS AND SUPPORT VECTOR MACHINES: THE TURKISH CASE
Fatih Ecer
Abstract
Experience from the banking crises during the past
two decades suggest that advanced prediction
models are needed for helping prevent bank failures.
This paper compares the ability of artificial neural
networks and support vector machines in predicting
bank failures. Although artificial neural networks have
widely been applied complex problems in business,
the literature utilizing support vector machines is
relatively narrow and their capability for predicting
bank failures is not very familiar. In this paper, these
two intelligent techniques are applied to a dataset of
Turkish commercial banks. Empirical findings show
that although the prediction performance of the
two models can be considered as satisfactory, neural
networks show slightly better predictive ability than
support vector machines. In addition, different types
of error from each model also indicate that neural
network models are better predictors.
Keywords
Current account; Balance of payments; ARDL; Bound Test
Hrčak ID:
109117
URI
Publication date:
1.10.2013.
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