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Core predictors of debt specialization:a new insight to optimal capital structure

2021Journal article Open access
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dc.contributor.authorKhan, Kanwal Iqbal
dc.contributor.authorQadeer, Faisal
dc.contributor.authorMata, Mário Nuno
dc.contributor.authorChavaglia Neto, José
dc.contributor.authorSabir, Qurat ul An
dc.contributor.authorMartins, Jéssica Nunes
dc.contributor.authorFilipe, José António
dc.date.accessioned2021-04-29T13:43:57Z
dc.date.available2021-04-29T13:43:57Z
dc.date.issued2021
dc.description.abstractDebt structure composition is an essential topic of discussion for the management of capital structure decisions. Researchers made extensive efforts to understand the criteria for selecting debts, specifically, to know about the reasons for debt specialization, concealed in identifying its predictors. This question is essential not only for establishing the field of debt structure but also for the financial managers to design corporate financial strategy in a way that leads to attaining an optimal debt structure. Sophisticated financial modeling is applied to identify the core predictors of debt specialization, influencing the strategic choices of optimal debt structure to address this issue. Data were collected from 419 non-financial companies listed at the Karachi Stock Exchange from 2009 to 2015. This study has validated debt specialization by showing that short-term debts maintain their position over the years and remain the most popular type of loan among Pakistani firms. Further, it provides a comprehensive view of the cross-sectional differences among the firms involved in debt specialization by applying a holistic approach. Results show that small, growing, dividend-paying companies, having high expense and risk ratios, followed the debt specialization strategy. This strategy enables firms to reduce their agency conflicts, transaction costs, information asymmetry, risk management and building up their good market reputation. Conclusively, we have identified the gross profit margin, long-term debt to asset ratio, firm size, age, asset tangibility, and long-term industry debt to asset ratio as reliable and core predictors of debt specialization for sustainable business growth.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationKhan, K. I., Qadeer, F., Mata, M. N., Chavaglia Neto, J., Sabir, Q. ul A., Martins, J. N., & Filipe, J. A. (2021). Core predictors of debt specialization: a new insight to optimal capital structure. Mathematics, 9(9). doi.: 10.3390/math9090975pt_PT
dc.identifier.doi10.3390/math9090975pt_PT
dc.identifier.issn2227-7390
dc.identifier.urihttp://hdl.handle.net/10400.15/3466
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.subjectDebt specializationpt_PT
dc.subjectCorporate financial strategypt_PT
dc.subjectOptimal debt structurept_PT
dc.subjectAgency conflictspt_PT
dc.subjectTransaction costpt_PT
dc.subjectInformation asymmetrypt_PT
dc.subjectFinancial modelingpt_PT
dc.subjectRisk managementpt_PT
dc.titleCore predictors of debt specialization:a new insight to optimal capital structurept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue9pt_PT
oaire.citation.titleMathematicspt_PT
oaire.citation.volume9pt_PT
person.familyNameMata
person.givenNameMário Nuno
person.identifier1403614
person.identifier.ciencia-idFA13-1761-4192
person.identifier.orcid0000-0003-1765-4273
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationda64bac4-a291-4dce-a167-d33ef827bce3
relation.isAuthorOfPublication.latestForDiscoveryda64bac4-a291-4dce-a167-d33ef827bce3

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