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A generalised regression neural network model of financing imbalance: Shari'ah compliance as the roadmap for sustainability of capital markets

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dc.contributor.authorHussain H.I.en_US
dc.contributor.authorAnwar N.A.M.en_US
dc.contributor.authorRazimi M.S.A.en_US
dc.date.accessioned2024-05-29T02:08:58Z
dc.date.available2024-05-29T02:08:58Z
dc.date.issued2020
dc.description.abstractThe current study looks at the impact of compliance to Shari'ah principles on the capital structure for Malaysian firms. Examination of impact of compliance is based on the classification by the Securities Commission of Malaysia. Given that the literature on adjustment tends to ignore non-linear models, the current study utilises Generalised Regression Neural Network (GRNNs). Results are compared to conventional panel data regression models via performing a hold-out sample. Initial results confirm stability of the data allowing predictive ability. The results indicate that compliant firms tend to finance a greater portion of their financing imbalance via equities relative to non-compliant firms. This provides a strong indication towards compliant firms reducing overall risk taking where the financing pattern incorporates a greater aspect of risk sharing which is in-line with Shari'ah principles. In addition, two more factors are ranked as important in deciding compliant firms issue choice to resolve financial imbalance: profitability and size. The rest of the determinants have low impact on explaining net debt issues. Diagnostics for results provide evidence of lower RMSE and MSE for GRNNs for the training, testing and overall datasets. The potential benefit of this research allows managers and investors of Islamic capital markets to understand potential risk exposure and financing costs of compliant firms. Findings also provide a roadmap for development of a sustainable capital market model which has wider implications on a global scale. � 2020-IOS Press and the authors.en_US
dc.identifier.citationJournal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5387-5395, 2020en_US
dc.identifier.doi10.3233/JIFS-189023
dc.identifier.epage5395
dc.identifier.issn10641246
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85094899448
dc.identifier.scopusWOS:000582322000056
dc.identifier.spage5387
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85094899448&doi=10.3233%2fJIFS-189023&partnerID=40&md5=f6e2706c1a12d0deedfeaebbd8f8f10e
dc.identifier.urihttps://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs189023
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10447
dc.identifier.volume39
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherIOS Press BVen_US
dc.relation.ispartofJournal Of Intelligent & Fuzzy Systemsen_US
dc.sourceScopus
dc.subjectCapital structureen_US
dc.subjectgeneralised regression neural networksen_US
dc.subjectIslamic capital marketsen_US
dc.subjectIslamic financeen_US
dc.subjectsustainable capital marketsen_US
dc.titleA generalised regression neural network model of financing imbalance: Shari'ah compliance as the roadmap for sustainability of capital marketsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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