Using The Artificial Neural Network (ANN) to Assess Bank Credit Risk: A Case Study of Indonesia
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.
|Date of creation:||Jul 2008|
|Date of revision:||Jul 2008|
|Contact details of provider:|| Postal: Loughborough, Leicestershire, LE11 3TU|
Phone: +44 (0) 1509 222701
Fax: +44 (0) 1509 223910
Web page: http://www.lboro.ac.uk/departments/sbe/research/economics/index.html
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Linda Allen & Anthony Saunders, 2003. "A survey of cyclical effects in credit risk measurement model," BIS Working Papers 126, Bank for International Settlements.
When requesting a correction, please mention this item's handle: RePEc:lbo:lbowps:2008_06. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Huw Edwards)
If references are entirely missing, you can add them using this form.