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

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

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Abstract

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.

Item Type: Article (Journal)
Additional Information: 152/70897
Uncontrolled Keywords: Sukuk; Ratings; Multinomial Logistic; Neural Network
Subjects: H Social Sciences > HG Finance
H Social Sciences > HG Finance > HG3368 Islamic Banking and Finance
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Institute of Islamic Banking & Finance (IIiBF)
Kulliyyah of Information and Communication Technology > Department of Information System
Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User: Dr. Mira Kartiwi
Date Deposited: 03 Apr 2019 16:14
Last Modified: 12 Jul 2019 11:45
URI: http://irep.iium.edu.my/id/eprint/70897

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