IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v34y2015icp273-292.html
   My bibliography  Save this article

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

Author

Listed:
  • Arundina, Tika
  • Azmi Omar, Mohd.
  • Kartiwi, Mira

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.

Suggested Citation

  • Arundina, Tika & Azmi Omar, Mohd. & Kartiwi, Mira, 2015. "The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 273-292.
    • Handle: RePEc:eee:pacfin:v:34:y:2015:i:c:p:273-292
    DOI: 10.1016/j.pacfin.2015.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X15000360
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.pacfin.2015.03.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
    2. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    3. Konan Chan & Narasimhan Jegadeesh, 2004. "Market-Based Evaluation for Models to Predict Bond Ratings," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 153-172.
    4. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    5. Wolfgang Härdle & Rouslan Moro & Dorothea Schäfer, 2006. "Graphical Data Representation in Bankruptcy Analysis," SFB 649 Discussion Papers SFB649DP2006-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Kamstra, Mark & Kennedy, Peter & Suan, Teck-Kin, 2001. "Combining Bond Rating Forecasts Using Logit," The Financial Review, Eastern Finance Association, vol. 36(2), pages 75-96, May.
    7. Hwang, Ruey-Ching & Chung, Huimin & Chu, C.K., 2010. "Predicting issuer credit ratings using a semiparametric method," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 120-137, January.
    8. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    9. Murat Körs & Ramazan Akta & M Mete Doanay, 2012. "Predicting the Bond Ratings of S&P 500 Firms," The IUP Journal of Applied Finance, IUP Publications, vol. 18(4), pages 83-96, October.
    10. Pinches, George E & Mingo, Kent A, 1973. "A Multivariate Analysis of Industrial Bond Ratings," Journal of Finance, American Finance Association, vol. 28(1), pages 1-18, March.
    11. Louis H. Ederington & Jess B. Yawitz & Brian E. Roberts, 1984. "The Informational Content of Bond Ratings," NBER Working Papers 1323, National Bureau of Economic Research, Inc.
    12. Kaplan, Robert S & Urwitz, Gabriel, 1979. "Statistical Models of Bond Ratings: A Methodological Inquiry," The Journal of Business, University of Chicago Press, vol. 52(2), pages 231-261, April.
    13. Horrigan, Jo, 1966. "Determination Of Long-Term Credit Standing With Financial Ratios," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 44-62.
    14. Intrator, Orna & Intrator, Nathan, 2001. "Interpreting neural-network results: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 37(3), pages 373-393, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paltrinieri, Andrea & Hassan, Mohammad Kabir & Bahoo, Salman & Khan, Ashraf, 2023. "A bibliometric review of sukuk literature," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 897-918.
    2. Runchi Zhang & Zhiyi Qiu, 2020. "Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-35, June.
    3. Ghlamallah, Ezzedine & Alexakis, Christos & Dowling, Michael & Piepenbrink, Anke, 2021. "The topics of Islamic economics and finance research," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 145-160.
    4. Ullah, Ayat & Arshad, Muhammad & Kächele, Harald & Zeb, Alam & Mahmood, Nasir & Müller, Klaus, 2020. "Socio-economic analysis of farmers facing asymmetric information in inputs markets: evidence from the rainfed zone of Pakistan," Technology in Society, Elsevier, vol. 63(C).
    5. Azmat, Saad & Skully, Michael & Brown, Kym, 2017. "The (little) difference that makes all the difference between Islamic and conventional bonds," Pacific-Basin Finance Journal, Elsevier, vol. 42(C), pages 46-59.
    6. Muhammad Safdar Sial & Jacob Cherian & Abdelrhman Meero & Asma Salman & Abdul Aziz Abdul Rahman & Sarminah Samad & Constantin Viorel Negrut, 2022. "Determining Financial Uncertainty through the Dynamics of Sukuk Bonds and Prices in Emerging Market Indices," Risks, MDPI, vol. 10(3), pages 1-13, March.
    7. Juan Carlos Reboredo & Nader Naifar, 2017. "Do Islamic Bond (Sukuk) Prices Reflect Financial and Policy Uncertainty? A Quantile Regression Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(7), pages 1535-1546, July.
    8. Naifar, Nader & Hammoudeh, Shawkat & Al dohaiman, Mohamed S., 2016. "Dependence structure between sukuk (Islamic bonds) and stock market conditions: An empirical analysis with Archimedean copulas," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 148-165.
    9. Jaspreet Kaur & Madhu Vij & Ajay Kumar Chauhan, 2023. "Signals influencing corporate credit ratings—a systematic literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(1), pages 91-114, March.
    10. Yuanxin Liu & FengYun Li & Xinhua Yu & Jiahai Yuan & Dong Zhou, 2018. "Assessing the Credit Risk of Corporate Bonds Based on Factor Analysis and Logistic Regress Analysis Techniques: Evidence from New Energy Enterprises in China," Sustainability, MDPI, vol. 10(5), pages 1-21, May.
    11. Ibrahim, Mansor H., 2015. "Issues in Islamic banking and finance: Islamic banks, Shari’ah-compliant investment and sukuk," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 185-191.
    12. Asutay, Mehmet & Hakim, Amira, 2018. "Exploring international economic integration through sukuk market connectivity: A network perspective," Research in International Business and Finance, Elsevier, vol. 46(C), pages 77-94.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ruey-Ching Hwang, 2013. "Forecasting credit ratings with the varying-coefficient model," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1947-1965, December.
    2. Shen, Chung-Hua & Huang, Yu-Li & Hasan, Iftekhar, 2012. "Asymmetric benchmarking in bank credit rating," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(1), pages 171-193.
    3. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
    4. Van Laere, Elisabeth & Baesens, Bart, 2010. "The development of a simple and intuitive rating system under Solvency II," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 500-510, June.
    5. Jaspreet Kaur & Madhu Vij & Ajay Kumar Chauhan, 2023. "Signals influencing corporate credit ratings—a systematic literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(1), pages 91-114, March.
    6. Mizen, Paul & Tsoukas, Serafeim, 2012. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," International Journal of Forecasting, Elsevier, vol. 28(1), pages 273-287.
    7. Manzoni, Katiuscia, 2004. "Modeling Eurobond credit ratings and forecasting downgrade probability," International Review of Financial Analysis, Elsevier, vol. 13(3), pages 277-300.
    8. Ruey‐Ching Hwang & K. F. Cheng & Cheng‐Few Lee, 2009. "On multiple‐class prediction of issuer credit ratings," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(5), pages 535-550, September.
    9. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    10. Hwang, Ruey-Ching & Chung, Huimin & Chu, C.K., 2010. "Predicting issuer credit ratings using a semiparametric method," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 120-137, January.
    11. Hirk, Rainer & Vana, Laura & Hornik, Kurt, 2022. "A corporate credit rating model with autoregressive errors," Journal of Empirical Finance, Elsevier, vol. 69(C), pages 224-240.
    12. Shen, Chung-Hua & Huang, Yu-Li & Hasan, Iftekhar, 2012. "Asymmetric benchmarking in bank credit rating," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(1), pages 171-193.
    13. Afef Feki Krichene & Walid Khoufi, 2016. "On the Nonlinearity of the Financial Ratios-Credit Ratings Relationship," Applied Finance and Accounting, Redfame publishing, vol. 2(2), pages 65-70, August.
    14. Ken Hung & Hui Wen Cheng & Shih-shen Chen & Ying-Chen Huang, 2013. "Factors that Affect Credit Rating: An Application of Ordered Probit Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 94-108, December.
    15. Shen, Chung-Hua & Huang, Yu-Li & Hasan, Iftekhar, 2012. "Asymmetric benchmarking in bank credit rating," Bank of Finland Research Discussion Papers 13/2012, Bank of Finland.
    16. Rubina Shaheen & Attiya Yasmin Javid, 2014. "Effect of Credit Rating on Firm Performance and Stock Return; Evidence form KSE Listed Firms," PIDE-Working Papers 2014:104, Pakistan Institute of Development Economics.
    17. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    18. repec:zbw:bofrdp:2012_013 is not listed on IDEAS
    19. Caporale, Guglielmo Maria & Matousek, Roman & Stewart, Chris, 2012. "Ratings assignments: Lessons from international banks," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1593-1606.
    20. Daniëls, H.A.M. & Kamp, B. & Verkooijen, W.J.H., 1997. "Application of Neural Networks to House Pricing and Bond Rating," Discussion Paper 1997-96, Tilburg University, Center for Economic Research.
    21. Koresh Galil, 2005. "Ratings as Predictors of Default in the Long Term:an Empirical Investigation," Working Papers 0505, Ben-Gurion University of the Negev, Department of Economics.

    More about this item

    Keywords

    Sukuk; Ratings; Multinomial Logistic; Neural Network;
    All these keywords.

    JEL classification:

    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G - Financial Economics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:pacfin:v:34:y:2015:i:c:p:273-292. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/pacfin .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.