The Validity of Multinomial Logistic Regression and Artificial Neural Network in Predicting Sukuk Rating: Evidence from Indonesian Stock Exchange

Muhammad Luqman Nurhakim (), Zainul Kisman and Faizah Syihab ()
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Muhammad Luqman Nurhakim: Fakultas Ekonomi dan Bisnis, Universitas Trilogi, Kalibata, Jakarta 12760, Indonesia
Zainul Kisman: Fakultas Ekonomi dan Bisnis, Universitas Trilogi, Kalibata, Jakarta 12760, Indonesia
Faizah Syihab: Fakultas Ekonomi dan Bisnis, Universitas Trilogi, Kalibata, Jakarta 12760, Indonesia

Review of Pacific Basin Financial Markets and Policies (RPBFMP), 2020, vol. 23, issue 04, 1-24

Abstract: The Sukuk (shariah bond) market is developing in Indonesia and potentially will capture the global market in the future. It is an attractive investment product and a hot current issue in the capital market. Especially, the problem of predicting an accurate and trustworthy rating. As the Sukuk market developed, the issue of Sukuk rating emerged. As ordinary investors will have difficulty predicting their ratings going forward, this research will provide solutions to the problems above. The objective of this study is to determine the Indonesian Sukuk rating determinants and comparing the Sukuk rating predictive model. This research uses Artificial Neural Network (ANN) and Multinomial Logistic Regression (MLR) as the predictive analysis model. Data in this study are collected by purposive sampling and employing Sukuk rated by PEFINDO, an Indonesian rating agency. Findings in this study are debt, profitability and firm size significantly affecting Sukuk rating category and the ANN performs better predictive accuracy than MLR. The implications of the results of the research for the issuer and bondholder are a higher level of credit enhancement, a higher level of profitability, and the bigger size of firm rewarding higher Sukuk rating.

Keywords: Sukuk rating; forecasting models; artificial neural network; multinomial logistic regression (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0219091520500320

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