Credit risk assessment: Evidence from banking industry
Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural netwo...
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Online Access: | http://www.growingscience.com/msl/Vol4/msl_2014_212.pdf |
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doaj-09c0160a3c4945ffb79aac8403b739482020-11-24T23:15:47ZengGrowing ScienceManagement Science Letters1923-29341923-93432014-08-01481765177210.5267/j.msl.2014.7.007Credit risk assessment: Evidence from banking industryHassan Ghodrati Gholamhassan Taghizad Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural network follows back propagation paradigm, which enables it to use historical data for predicting future values with very good out of sample fitting. Macroeconomic variables including GDP, exchange rate, inflation rate, stock price index, and M2 are used to forecast credit risk for two Iranian banks; namely Saderat and Sarmayeh over the period 2007-2011. Research data are being tested for ADF and Causality Granger tests before entering the ANN to achieve the best lag structure for the research model. MSE and R values for the developed ANN in this research respectively are 86×〖10〗^(-4) and 0.9885, respectively. The results showed that ANN was able to predict banks’ credit risk with low error. Sensibility analyses which has accomplished on this research’s ANN corroborates that M2 has the highest effect on the ANN’s credit risk and should be considered as an additional leading indicator by Iran’s banking authorities. These matters confirm validation of macroeconomic notions in Iran’s credit systematic risk.http://www.growingscience.com/msl/Vol4/msl_2014_212.pdfCredit RiskArtificial Neural NetworkDefault RiskMacroeconomic VariablesIranian banks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hassan Ghodrati Gholamhassan Taghizad |
spellingShingle |
Hassan Ghodrati Gholamhassan Taghizad Credit risk assessment: Evidence from banking industry Management Science Letters Credit Risk Artificial Neural Network Default Risk Macroeconomic Variables Iranian banks |
author_facet |
Hassan Ghodrati Gholamhassan Taghizad |
author_sort |
Hassan Ghodrati |
title |
Credit risk assessment: Evidence from banking industry |
title_short |
Credit risk assessment: Evidence from banking industry |
title_full |
Credit risk assessment: Evidence from banking industry |
title_fullStr |
Credit risk assessment: Evidence from banking industry |
title_full_unstemmed |
Credit risk assessment: Evidence from banking industry |
title_sort |
credit risk assessment: evidence from banking industry |
publisher |
Growing Science |
series |
Management Science Letters |
issn |
1923-2934 1923-9343 |
publishDate |
2014-08-01 |
description |
Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural network follows back propagation paradigm, which enables it to use historical data for predicting future values with very good out of sample fitting. Macroeconomic variables including GDP, exchange rate, inflation rate, stock price index, and M2 are used to forecast credit risk for two Iranian banks; namely Saderat and Sarmayeh over the period 2007-2011. Research data are being tested for ADF and Causality Granger tests before entering the ANN to achieve the best lag structure for the research model. MSE and R values for the developed ANN in this research respectively are 86×〖10〗^(-4) and 0.9885, respectively. The results showed that ANN was able to predict banks’ credit risk with low error. Sensibility analyses which has accomplished on this research’s ANN corroborates that M2 has the highest effect on the ANN’s credit risk and should be considered as an additional leading indicator by Iran’s banking authorities. These matters confirm validation of macroeconomic notions in Iran’s credit systematic risk. |
topic |
Credit Risk Artificial Neural Network Default Risk Macroeconomic Variables Iranian banks |
url |
http://www.growingscience.com/msl/Vol4/msl_2014_212.pdf |
work_keys_str_mv |
AT hassanghodrati creditriskassessmentevidencefrombankingindustry AT gholamhassantaghizad creditriskassessmentevidencefrombankingindustry |
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1725589500436938752 |