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:	HG Finance HG3368 Islamic Banking and Finance
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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recordtype	eprints
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
_version_	1777413115847114752

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

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