Islam, Gazi Taufiq and Malakar, Surajit and Hassan, Khondekar Lutful and Dey, Rajesh and Mahajan, Rupali A and Kassim, Salina (2024) A comparative analysis of machine learning approaches in sukuk price estimation across global regions. Social Science Research Network (SSRN). pp. 1-10.
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Abstract
Sukuk, also known as Islamic bonds, constitute a significant aspect of Islamic finance, offering Shariah-compliant investment opportunities. Motivated by the increasing prominence of Sukuk in global financial markets and their potential for economic development, this study aims to investigate the effectiveness of machine learning neural networks in Sukuk price estimation. The objective is to evaluate the accuracy and efficiency of various machine learning techniques across diverse global regions with significant interest in Sukuk investment, as determined by the size of the Muslim population. The methodology for literature selection involves a systematic search of academic databases and scholarly repositories, focusing on recent publications within the last five years. Search terms include keywords related to Sukuk and machine learning. Selected papers are screened based on titles and abstracts to ensure relevance to the research topic, prioritizing those that explicitly discuss both Sukuk and machine learning. In addition, articles are evaluated for outcome-based research, particularly those that offer conclusions about the precision and effectiveness of Sukuk pricing or machine learning-based forecasting. The findings suggest that artificial neural networks perform better than traditional statistical methods in Sukuk price estimation. However, restrictions including short dataset sizes, the omission of Sukuk backed by assets, and overly basic rating categories indicate areas that warrant additional investigation. Future studies could explore comparative analyses of different machine learning algorithms, refine models for dynamic market conditions, and incorporate real-time data integration to enhance Sukuk price forecasting accuracy. Considering these drawbacks, the results highlight how machine learning might enhance the effectiveness and precision of Sukuk pricing.
| Item Type: | Article (Journal) |
|---|---|
| Uncontrolled Keywords: | Sukuk, Machine Learning, Predictions, Islamic Finance |
| 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) |
| Depositing User: | Prof. Dr. Salina Kassim |
| Date Deposited: | 23 Dec 2024 09:51 |
| Last Modified: | 23 Dec 2024 09:51 |
| URI: | http://irep.iium.edu.my/id/eprint/116838 |
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