The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences

The development of Sukuk market as the alternative to the existing conventional bond market has risen the issue of rating the Sukuk issuance. These credit ratings fulfill a key function of information transmission in capital market. Moreover, Basel Committee for Banking Supervision has now institute...

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Main Authors: Arundina, Tika, Omar, Mohd. Azmi, Kartiwi, Mira
Format: Article
Language:English
English
Published: Elsevier 2015
Subjects:
Online Access:http://irep.iium.edu.my/70897/
http://irep.iium.edu.my/70897/
http://irep.iium.edu.my/70897/
http://irep.iium.edu.my/70897/7/70897%20The%20Predictive%20Accuracy%20of%20Sukuk%20Ratings.pdf
http://irep.iium.edu.my/70897/8/70897%20The%20Predictive%20Accuracy%20of%20Sukuk%20Ratings%20SCOPUS.pdf
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spelling iium-708972019-07-12T03:45:39Z http://irep.iium.edu.my/70897/ The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences Arundina, Tika Omar, Mohd. Azmi Kartiwi, Mira HG Finance HG3368 Islamic Banking and Finance The development of Sukuk market as the alternative to the existing conventional bond market has risen the issue of rating the Sukuk issuance. These credit ratings fulfill a key function of information transmission in capital market. Moreover, Basel Committee for Banking Supervision has now instituted capital charges for credit risk based on credit ratings. Basel II framework allowed the bank to establish capital adequacy requirements based on ratings provided by external credit rating agencies or determine rating of its investment internally for more advance approach. For these reasons, ratings are considered important by issuers, investors, and regulators alike. This study provides an empirical foundation for the investors to estimate the ratings assigned using the approach from several rating agencies and past researches on bond ratings. It tries to compare the accuracy of two logistic models; Multinomial Logistic Regression and Neural Network to create a model of rating probability from several financial variables. Elsevier 2015-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/70897/7/70897%20The%20Predictive%20Accuracy%20of%20Sukuk%20Ratings.pdf application/pdf en http://irep.iium.edu.my/70897/8/70897%20The%20Predictive%20Accuracy%20of%20Sukuk%20Ratings%20SCOPUS.pdf Arundina, Tika and Omar, Mohd. Azmi and Kartiwi, Mira (2015) The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences. Pacific-Basin Finance Journal, 34. pp. 273-292. ISSN 0927-538X https://www.journals.elsevier.com/pacific-basin-finance-journal 10.1016/j.pacfin.2015.03.002
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic HG Finance
HG3368 Islamic Banking and Finance
spellingShingle HG Finance
HG3368 Islamic Banking and Finance
Arundina, Tika
Omar, Mohd. Azmi
Kartiwi, Mira
The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences
description The development of Sukuk market as the alternative to the existing conventional bond market has risen the issue of rating the Sukuk issuance. These credit ratings fulfill a key function of information transmission in capital market. Moreover, Basel Committee for Banking Supervision has now instituted capital charges for credit risk based on credit ratings. Basel II framework allowed the bank to establish capital adequacy requirements based on ratings provided by external credit rating agencies or determine rating of its investment internally for more advance approach. For these reasons, ratings are considered important by issuers, investors, and regulators alike. This study provides an empirical foundation for the investors to estimate the ratings assigned using the approach from several rating agencies and past researches on bond ratings. It tries to compare the accuracy of two logistic models; Multinomial Logistic Regression and Neural Network to create a model of rating probability from several financial variables.
format Article
author Arundina, Tika
Omar, Mohd. Azmi
Kartiwi, Mira
author_facet Arundina, Tika
Omar, Mohd. Azmi
Kartiwi, Mira
author_sort Arundina, Tika
title The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences
title_short The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences
title_full The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences
title_fullStr The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences
title_full_unstemmed The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences
title_sort predictive accuracy of sukuk ratings; multinomial logistic and neural network inferences
publisher Elsevier
publishDate 2015
url http://irep.iium.edu.my/70897/
http://irep.iium.edu.my/70897/
http://irep.iium.edu.my/70897/
http://irep.iium.edu.my/70897/7/70897%20The%20Predictive%20Accuracy%20of%20Sukuk%20Ratings.pdf
http://irep.iium.edu.my/70897/8/70897%20The%20Predictive%20Accuracy%20of%20Sukuk%20Ratings%20SCOPUS.pdf
first_indexed 2023-09-18T21:40:38Z
last_indexed 2023-09-18T21:40:38Z
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