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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/4180

Title: Using the Artificial Neural Network (ANN) to assess bank credit risk: a case study of Indonesia
Authors: Hall, Maximilian J.B.
Muljawan, Dadang
Suprayogi
Moorena, Lolita
Keywords: Default risk
Artificial neural network
Bayesian regularization
Transition matrix
Issue Date: 2008
Publisher: © Loughborough University
Series/Report no.: Loughborough University. Department of Economics. Discussion Paper Series;WP 2008 - 06
Abstract: Ever since the Asian Financial Crisis, concerns have risen over whether policy-makers have sufficient tools to maintain financial stability. The ability to predict financial disturbances enables the authorities to take precautionary action to minimize their impact. In this context, the authorities may use any financial indicators which may accurately predict shifts in the quality of bank exposures. This paper uses key macro-economic variables (i.e. GDP growth, the inflation rate, stock prices, the exchange rates, and money in circulation) to predict the default rate of the Indonesian Islamic banks’ exposures. The default rates are forecasted using the Artificial Neural Network (ANN) methodology, which incorporates the Bayesian Regularization technique. From the sensitivity analysis, it is shown that stock prices could be used as a leading indicator of future problem.
Description: This is a working paper. It is also available at: http://ideas.repec.org/p/lbo/lbowps/2008_06.html
Version: Published
URI: https://dspace.lboro.ac.uk/2134/4180
ISSN: 1750-4171
Appears in Collections:Working Papers (Economics)

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