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2016 | OriginalPaper | Chapter

8. Artificial Intelligence for Islamic Sukuk Rating Predictions

Authors : Tika Arundina, Mira Kartiwi, Mohd. Azmi Omar

Published in: Artificial Intelligence in Financial Markets

Publisher: Palgrave Macmillan UK

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Abstract

The development of a Sukuk market as the alternative to the existing conventional bond market has given rise to an issue of the availability and accuracy of some of Sukuk issuance’s ratings. This results in asymmetric information among capital market players, which ultimately needs to be reduced. Moreover, the Basel III framework allows banks to establish capital adequacy requirements based on ratings provided by external credit rating agencies or, in the absence of these, to determine ratings of its investment internally. For these reasons, ratings are considered important by issuers, investors and regulators alike. Focusing on Malaysian outstanding long-term corporate Sukuk in the year 2012, this study tests the efficacy and accuracy of the Sukuk rating model when compared to the ratings assigned using a Multinomial Logistic Regression model, a Decision Tree and a Neural Network.
In order to address the limited study on Sukuk rating prediction, this research provides an empirical foundation for the investors to estimate the ratings assigned. The study examines variables from past research on rating prediction models taking into account various Sukuk structures, credit enhancement facilities, the industrial sector and macroeconomic variables. All methods strongly indicate that share price, Sukuk structure and guarantee status are empirically proven to be key factors when predicting Sukuk ratings. Furthermore, neural network methods obtain the highest accuracy rate when predicting the actual rating in the market 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 investor to support their investment decision making. The regulatory agencies may also take advantage of these models as they can be used as a benchmark for the Internal Rating Based) approach as required in Basel III. In line with those practical implications, this study also aims to contribute to the development of an Islamic finance body of knowledge.

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Appendix
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Literature
1.
go back to reference Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004). Credit rating analysis with support vector machine and neural networks: A market comparative study. Decision Support System, 37, 543–558.CrossRef Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004). Credit rating analysis with support vector machine and neural networks: A market comparative study. Decision Support System, 37, 543–558.CrossRef
2.
3.
go back to reference Mizen, P., & Tsoukas, S. (2012). Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model. International Journal of Forecasting, 28(1), 273–287.CrossRef Mizen, P., & Tsoukas, S. (2012). Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model. International Journal of Forecasting, 28(1), 273–287.CrossRef
4.
go back to reference Novotna, M. (2012, Sept). The use of different approaches for credit rating prediction and their comparison. In Proceedings 6th International Scientific Conference Managing and Modelling of Financial Risks (pp. 448–457). Ostrava. Novotna, M. (2012, Sept). The use of different approaches for credit rating prediction and their comparison. In Proceedings 6th International Scientific Conference Managing and Modelling of Financial Risks (pp. 448–457). Ostrava.
5.
go back to reference Dainelli, F., Giunta, F., & Cipollini, F. (2013). Determinants of SME credit worthiness under Basel rules: the value of credit history information. PSL Quarterly Review, 66, 21–47. Dainelli, F., Giunta, F., & Cipollini, F. (2013). Determinants of SME credit worthiness under Basel rules: the value of credit history information. PSL Quarterly Review, 66, 21–47.
7.
go back to reference Muscettola, M., & Naccarato, F. (2013). Probability of default and probability of excellence, an Inverse Model of Rating. One more tool to overcome the crisis: An empirical analysis. Business System Review, 2(2). Muscettola, M., & Naccarato, F. (2013). Probability of default and probability of excellence, an Inverse Model of Rating. One more tool to overcome the crisis: An empirical analysis. Business System Review, 2(2).
8.
go back to reference Doumpos, M., Niklis, D., Zopounidis, C., & Andriosopoulos, K. (2014). Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms. Journal of Banking & Finance, 50, 1–9. Doumpos, M., Niklis, D., Zopounidis, C., & Andriosopoulos, K. (2014). Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms. Journal of Banking & Finance, 50, 1–9.
9.
go back to reference Arundina, T. and Mohd Azmi Omar. (2010). Determinant of Sukuk Rating; The Case of Malaysia. Thesis Kulliyah of Economics and Management Science, International Islamic University Malaysia. Arundina, T. and Mohd Azmi Omar. (2010). Determinant of Sukuk Rating; The Case of Malaysia. Thesis Kulliyah of Economics and Management Science, International Islamic University Malaysia.
10.
go back to reference Tariq, A.A. (2004). Managing financial risks of Sukuk structures. Unpublished Dissertation, Loughborough University, Leicestershire, United Kingdom. Tariq, A.A. (2004). Managing financial risks of Sukuk structures. Unpublished Dissertation, Loughborough University, Leicestershire, United Kingdom.
11.
go back to reference AAOIFI. (2005). Accounting and Auditing Organization of Islamic Financial Institutions, Shari’ah Standards. AAOIFI. (2005). Accounting and Auditing Organization of Islamic Financial Institutions, Shariah Standards.
12.
go back to reference Maya Puspa Rahman and Mohd Azmi Omar. (2012, July). Factors influencing the excess returns of Sukuk: An empirical analysis on USD denominated Sukuk Issued by Corporations in the Gulf Cooperation Council (GCC). Paper presented in the 3rd Gulf Research Meeting, University of Cambridge, Cambridge, United Kingdom. Maya Puspa Rahman and Mohd Azmi Omar. (2012, July). Factors influencing the excess returns of Sukuk: An empirical analysis on USD denominated Sukuk Issued by Corporations in the Gulf Cooperation Council (GCC). Paper presented in the 3rd Gulf Research Meeting, University of Cambridge, Cambridge, United Kingdom.
13.
go back to reference Fitch, R. (2011). Rating Sukuk Cross-Sector Criteria. London: Author. Fitch, R. (2011). Rating Sukuk Cross-Sector Criteria. London: Author.
14.
go back to reference Standard and Poor’s. (2007). Standard and poor’s approach to rating Sukuk. Standard and Poor’s Ratings Direct. Paris: Author. Standard and Poor’s. (2007). Standard and poors approach to rating Sukuk. Standard and Poor’s Ratings Direct. Paris: Author.
15.
go back to reference Moody’s Investor Service. (2006) Shari’ah and Sukuk: A Moody’s Primer. Moody’s Investors Service. International Structured Finance. London: Author Moody’s Investor Service. (2006) Shariah and Sukuk: A Moodys Primer. Moody’s Investors Service. International Structured Finance. London: Author
16.
go back to reference RAM Rating Service Berhad. (2011). General approach to Sukuk Rating. Criteria and methodology. Kuala Lumpur: Author. RAM Rating Service Berhad. (2011). General approach to Sukuk Rating. Criteria and methodology. Kuala Lumpur: Author.
17.
go back to reference Malaysian Rating Corporation Berhad (MARC). (2012). Islamic Financial Institutions. MARC Rating Methodology. Kuala Lumpur: Author. Malaysian Rating Corporation Berhad (MARC). (2012). Islamic Financial Institutions. MARC Rating Methodology. Kuala Lumpur: Author.
18.
go back to reference Horrigan, J. (1966). The determination of long term credit sharing with financial ratios. Journal of Accounting Research, 4, 44–62.CrossRef Horrigan, J. (1966). The determination of long term credit sharing with financial ratios. Journal of Accounting Research, 4, 44–62.CrossRef
19.
go back to reference West, R. (1970). An alternative approach to predicting corporate bond rating. Journal of Accounting Research, 7, 118–127.CrossRef West, R. (1970). An alternative approach to predicting corporate bond rating. Journal of Accounting Research, 7, 118–127.CrossRef
20.
go back to reference Pinches, G., & Mingo, K. (1973). A multivariate analysis of industrial bond ratings. Journal of Finance, 28, 1–18.CrossRef Pinches, G., & Mingo, K. (1973). A multivariate analysis of industrial bond ratings. Journal of Finance, 28, 1–18.CrossRef
21.
go back to reference Belkaoui, A. (1980). Industrial bond ratings: A new look. Financial Management. Autumn, 99, 44–51.CrossRef Belkaoui, A. (1980). Industrial bond ratings: A new look. Financial Management. Autumn, 99, 44–51.CrossRef
22.
go back to reference Dutta, S., & Shekar, S. (1988). Bond rating: A non-conservative application of neural networks. IEEE International Conference on Neural Networks, 2, 443–450.CrossRef Dutta, S., & Shekar, S. (1988). Bond rating: A non-conservative application of neural networks. IEEE International Conference on Neural Networks, 2, 443–450.CrossRef
23.
go back to reference Chaveesuk, R., Srivaree-ratana, C., & Smith, E. (1997). Alternative neural network approaches to corporate bond rating. Journal of Engineering Valuation and Cost Analysis, 2(2), 117–131. Chaveesuk, R., Srivaree-ratana, C., & Smith, E. (1997). Alternative neural network approaches to corporate bond rating. Journal of Engineering Valuation and Cost Analysis, 2(2), 117–131.
24.
go back to reference Singleton, J. C., & Surkan, A. J. (1995). Bond rating with neural networks. In A. Refens (Ed.), Neural networks in the capital markets. New York: John Wiley. Singleton, J. C., & Surkan, A. J. (1995). Bond rating with neural networks. In A. Refens (Ed.), Neural networks in the capital markets. New York: John Wiley.
25.
go back to reference Kwon, Y. S., Han, I., & Lee, K. C. (1997). Ordinal pairwise partitioning (OPP) approach to neural networks training in bond rating. Intelligent Systems in Accounting, Finance and Management, 6, 23–40.CrossRef Kwon, Y. S., Han, I., & Lee, K. C. (1997). Ordinal pairwise partitioning (OPP) approach to neural networks training in bond rating. Intelligent Systems in Accounting, Finance and Management, 6, 23–40.CrossRef
27.
go back to reference Körs, M., Akta, R., & Do, M. M. (2012). Predicting the bond ratings of S & P 500 firms. IUP Journal of Applied Finance, 18, 83–96. Körs, M., Akta, R., & Do, M. M. (2012). Predicting the bond ratings of S & P 500 firms. IUP Journal of Applied Finance, 18, 83–96.
28.
go back to reference Bauer, J. and Agarwal, V. (2014). Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test. Journal of Banking & Finance, 40, 432–442. Bauer, J. and Agarwal, V. (2014). Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test. Journal of Banking & Finance, 40, 432–442.
29.
go back to reference Galil, K., & Sher, N. (2015). Predicting default more accurately: To proxy or not to proxy for default? Beersheba, Israel: Ben-Gurion University of the Negev. Galil, K., & Sher, N. (2015). Predicting default more accurately: To proxy or not to proxy for default? Beersheba, Israel: Ben-Gurion University of the Negev.
30.
go back to reference Touray, A. K. (2004). Predicting a bond rating: Multivariate analysis of corporate bonds, a new look at Malaysian corporate bonds. Unpublished master thesis, International Islamic University of Malaysia, Kuala Lumpur. Touray, A. K. (2004). Predicting a bond rating: Multivariate analysis of corporate bonds, a new look at Malaysian corporate bonds. Unpublished master thesis, International Islamic University of Malaysia, Kuala Lumpur.
31.
go back to reference Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.CrossRef Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.CrossRef
32.
go back to reference Du, Y. (2003). Predicting credit rating and credit rating changes : A new approach. Unpublished master thesis, Queen’s School of Business, Kingston, Ontario. Du, Y. (2003). Predicting credit rating and credit rating changes : A new approach. Unpublished master thesis, Queen’s School of Business, Kingston, Ontario.
33.
go back to reference Cao, L., Kian, L., & Jingqing, Z. (2006). Bond rating using support vector machine. Intelligent Data Analysis, 10(70501008), 285–296. Cao, L., Kian, L., & Jingqing, Z. (2006). Bond rating using support vector machine. Intelligent Data Analysis, 10(70501008), 285–296.
34.
go back to reference Menard, S. (1995). Applied logistic regression analysis. Sage University Paper series on Quatitative Applications in the Social Sciences (pp. 07–106). Thousand Oaks, CA: Sage. Menard, S. (1995). Applied logistic regression analysis. Sage University Paper series on Quatitative Applications in the Social Sciences (pp. 07–106). Thousand Oaks, CA: Sage.
35.
go back to reference Agresti, A. (1996). An introduction to categorical data analysis. New York: John Wiley & Sons, Inc.. Agresti, A. (1996). An introduction to categorical data analysis. New York: John Wiley & Sons, Inc..
36.
go back to reference Hosmer, D. L., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: John Willy & Sons Inc..CrossRef Hosmer, D. L., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: John Willy & Sons Inc..CrossRef
37.
go back to reference Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth.
38.
go back to reference Scheffer, T. (2001). Finding association rules that trade support optimally against confidence. In L. de Raedt & A. Siebes (Eds.), Proceedings of the Fifth European Conference on Principles of Data Mining and Knowledge Discovery (pp. 424–435). Freiburg, Germany, Berlin: Springer.CrossRef Scheffer, T. (2001). Finding association rules that trade support optimally against confidence. In L. de Raedt & A. Siebes (Eds.), Proceedings of the Fifth European Conference on Principles of Data Mining and Knowledge Discovery (pp. 424–435). Freiburg, Germany, Berlin: Springer.CrossRef
39.
go back to reference Bhargava, N., Bhargave, R., & Mathuria, M. (2013). Decision tree analysis on J48 Algorithm for Data Mining. International Journal of Advanced Research in Computer Science and Software Engineering, 3(6), 1114–1119. Bhargava, N., Bhargave, R., & Mathuria, M. (2013). Decision tree analysis on J48 Algorithm for Data Mining. International Journal of Advanced Research in Computer Science and Software Engineering, 3(6), 1114–1119.
40.
go back to reference Rokach, L., & Maimon, O. (2008). Data mining with decision trees. Theory and applications. Singapore: World Scientific Publishing. Rokach, L., & Maimon, O. (2008). Data mining with decision trees. Theory and applications. Singapore: World Scientific Publishing.
41.
go back to reference Witzany, J. (2010). Credit risk management and modelling. Praha: Oeconomica. Witzany, J. (2010). Credit risk management and modelling. Praha: Oeconomica.
42.
go back to reference Witten, I. H., Frank, E., & Mark, A. H. (2011). Data mining practical machine learning tools and techniques (3 ed.). Burlington, USA: Elsevier. Witten, I. H., Frank, E., & Mark, A. H. (2011). Data mining practical machine learning tools and techniques (3 ed.). Burlington, USA: Elsevier.
43.
go back to reference Coats, P. K., & Fant, L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management (Autumn), 22, 142–155.CrossRef Coats, P. K., & Fant, L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management (Autumn), 22, 142–155.CrossRef
44.
go back to reference Gambrel, P. A. (2004). The impact of cash flow on business failure. FL: Nova Southeastern University. Gambrel, P. A. (2004). The impact of cash flow on business failure. FL: Nova Southeastern University.
45.
go back to reference Altman, G. M., & Varetto, F. (1994). Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18, 505–529.CrossRef Altman, G. M., & Varetto, F. (1994). Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18, 505–529.CrossRef
46.
go back to reference Nazzal, J. M., El-emary, I. M., Najim, S. A., & Ahliyya, A. (2008). Multilayer Perceptron Neural Network (MLPs) For Analyzing the Propoerties of Jordan Oil Shale. World Applied Sciences Journal, 5(5), 546–552. Nazzal, J. M., El-emary, I. M., Najim, S. A., & Ahliyya, A. (2008). Multilayer Perceptron Neural Network (MLPs) For Analyzing the Propoerties of Jordan Oil Shale. World Applied Sciences Journal, 5(5), 546–552.
47.
go back to reference Hair Jr., J. F., et al. (2006). Mutivariate Data Analysis (6 ed.). New Jersey: Prentice-Hall, Inc. Hair Jr., J. F., et al. (2006). Mutivariate Data Analysis (6 ed.). New Jersey: Prentice-Hall, Inc.
48.
go back to reference Norusis, M. J. (2009). SPSS Regression Models 17.0. SPSS Inc. Norusis, M. J. (2009). SPSS Regression Models 17.0. SPSS Inc.
49.
go back to reference Kim, J. W., Weistroffer, H. R., & Redmond, R. T. (1993). Expert systems for bond rating: a comparative analysis of statistical, rule-based and neural network systems. Expert System, 10(3), 167–172.CrossRef Kim, J. W., Weistroffer, H. R., & Redmond, R. T. (1993). Expert systems for bond rating: a comparative analysis of statistical, rule-based and neural network systems. Expert System, 10(3), 167–172.CrossRef
50.
go back to reference Kim, K and Han, I. (2001). The cluster-indexing method for case-based reasoning using self-organizing maps and learning vector quantization for bond rating cases. Elsevier Science-Expert System with Applications. pp. 147–156. Kim, K and Han, I. (2001). The cluster-indexing method for case-based reasoning using self-organizing maps and learning vector quantization for bond rating cases. Elsevier Science-Expert System with Applications. pp. 147–156.
51.
go back to reference Intrator, O., & Intrator, N. (2001). Interpreting neural network results: A simulation study. Computational Statistics & Data Analysis, 37, 373–393.CrossRef Intrator, O., & Intrator, N. (2001). Interpreting neural network results: A simulation study. Computational Statistics & Data Analysis, 37, 373–393.CrossRef
52.
go back to reference Koh, H.C. (1992). The sensitivity of optimal cutoff points to misclassification costs of type I and II errors in the going-concern prediction context. Journal of Business Finance & Accounting, 19(2), 187–197. Koh, H.C. (1992). The sensitivity of optimal cutoff points to misclassification costs of type I and II errors in the going-concern prediction context. Journal of Business Finance & Accounting, 19(2), 187–197.
53.
go back to reference Ederington, L. H. Yawitz, J. B. and Roberts, B. E. (1984). The informational content of bond ratings. Working Paper Series National Bureau of Economic Research. Cambridge. Ederington, L. H. Yawitz, J. B. and Roberts, B. E. (1984). The informational content of bond ratings. Working Paper Series National Bureau of Economic Research. Cambridge.
54.
go back to reference Maher, J. J., & Sen, T. K. (1997). Predicting bond ratings using neural networks: A comparison with logistic regression. Intelligent Systems in Accounting, Finance and Management, 6, 59–72.CrossRef Maher, J. J., & Sen, T. K. (1997). Predicting bond ratings using neural networks: A comparison with logistic regression. Intelligent Systems in Accounting, Finance and Management, 6, 59–72.CrossRef
55.
go back to reference Hu, Y. and Ansell, J. (2005). Developing Financial Distress Prediction Models: A study of US, Europe and Japan Retail Performance. Working paper series, University of Edinburgh. http://papers.ssrn.com (2009). Hu, Y. and Ansell, J. (2005). Developing Financial Distress Prediction Models: A study of US, Europe and Japan Retail Performance. Working paper series, University of Edinburgh. http://​papers.​ssrn.​com (2009).
56.
go back to reference Kamstra, M., Kennedy, P., & Suan, T. (2001). Combining bond rating forecasts using Logit. The Financial Review, 37, 75–96.CrossRef Kamstra, M., Kennedy, P., & Suan, T. (2001). Combining bond rating forecasts using Logit. The Financial Review, 37, 75–96.CrossRef
57.
go back to reference Kim, K. S. (2005). Predicting bond ratings using publicly available information. Expert System with Applications, 29, 75–81.CrossRef Kim, K. S. (2005). Predicting bond ratings using publicly available information. Expert System with Applications, 29, 75–81.CrossRef
58.
go back to reference Brabazon, A. and Neill, M. O. (2006). Bond rating using grammatical evolution. Springer Link. Volume 3005 Brabazon, A. and Neill, M. O. (2006). Bond rating using grammatical evolution. Springer Link. Volume 3005
59.
go back to reference Hwang, R., Cheng, K. F., & Lee, C. (2009). On multiple-class prediction of issuer credit ratings. Applied Stcochastic Models in Business and Industry, 25, 535–550. doi:10.1002/asmb.CrossRef Hwang, R., Cheng, K. F., & Lee, C. (2009). On multiple-class prediction of issuer credit ratings. Applied Stcochastic Models in Business and Industry, 25, 535–550. doi:10.​1002/​asmb.CrossRef
Metadata
Title
Artificial Intelligence for Islamic Sukuk Rating Predictions
Authors
Tika Arundina
Mira Kartiwi
Mohd. Azmi Omar
Copyright Year
2016
DOI
https://doi.org/10.1057/978-1-137-48880-0_8