Asset-based Sukuk Rating Prediction

Released on by Tika Arundina
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  • Asset-based Sukuk Rating Prediction Book Detail

  • Author : Tika Arundina
  • Release Date : 2015
  • Publisher :
  • Genre : Bonds
  • Pages : 554
  • ISBN 13 :
  • File Size : 51,51 MB
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Asset-based Sukuk Rating Prediction by Tika Arundina PDF Summary

Book Description: The development of sukuk market as the alternative to the existing conventional bond market has risen the issue of sukuk issuance's rating. These credit ratings fulfil a key function of information transmission in capital market. Issuers seek ratings for a number of reasons, including to improve the trust of their business counterparties or because they wish to sell securities to investors with preferences over ratings. Moreover, Basel Committee for Banking Supervision has now instituted capital charges for credit risk based on credit ratings. Basel II framework allows 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. Focusing on Malaysian outstanding long-term corporate sukuk from the period of 2001 to 2012, this study tries to test the efficacy and accuracy of sukuk rating model compared to the actual rating assigned by Malaysian rating agencies using statistical and data mining method, namely: Multinomial Logistic Regression, Decision Tree and Neural Network. To address the limited study on sukuk rating prediction, this research provides an empirical foundation for the investors to estimate the sukuk ratings assigned. The study examines variables from past researches on bond ratings, corporate ratings and financial distress prediction model taking into account on the various sukuk structure, credit enhancement facilities, industrial sector and macroeconomics variables. Interestingly, both statistical and data mining methods strongly indicate that share price, sukuk structure and guarantee status are empirically proven as key factors to predict sukuk rating. In addition, neural network method obtains the highest accuracy rate to predict the actual rating in the market as compared to the other two methods. Therefore, it is expected that the proposed models are beneficial to the rating agencies, sukuk issuer companies, corporate managers, private and institutional investors to support their investment decision making. The regulatory agencies may also take advantage to consider this model as benchmark for Internal Rating Based (IRB) approach as required in Basel II. In line with those practical implications, this study is also aimed to contribute the novelty aspects in the body of Islamic finance.

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