Core predictors of debt specialization

Detalhes bibliográficos
Autor(a) principal: Khan, Kanwal Iqbal
Data de Publicação: 2021
Outros Autores: Qadeer, Faisal, Mata, Mário Nuno, Chavaglia Neto, José, Sabir, Qurat Ul An, Martins, Jéssica Nunes, Filipe, José António
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/118262
Resumo: Khan, K. I., Qadeer, F., Mata, M. N., Chavaglia Neto, J., Sabir, Q. U. A., Martins, J. N., & Filipe, J. A. (2021). Core predictors of debt specialization: A new insight to optimal capital structure. Mathematics, 9(9), 1-25. [975]. https://doi.org/10.3390/math9090975
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spelling Core predictors of debt specializationA new insight to optimal capital structureAgency conflictsCorporate financial strategyDebt specializationFinancial modelingInformation asymmetryOptimal debt structureRisk managementTransaction costMathematics(all)Khan, K. I., Qadeer, F., Mata, M. N., Chavaglia Neto, J., Sabir, Q. U. A., Martins, J. N., & Filipe, J. A. (2021). Core predictors of debt specialization: A new insight to optimal capital structure. Mathematics, 9(9), 1-25. [975]. https://doi.org/10.3390/math9090975Debt 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.NOVA Information Management School (NOVA IMS)RUNKhan, Kanwal IqbalQadeer, FaisalMata, Mário NunoChavaglia Neto, JoséSabir, Qurat Ul AnMartins, Jéssica NunesFilipe, José António2021-05-25T00:58:01Z2021-05-012021-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article25application/pdfhttp://hdl.handle.net/10362/118262eng2227-7390PURE: 31443504https://doi.org/10.3390/math9090975info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:01:11Zoai:run.unl.pt:10362/118262Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:50.879196Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Core predictors of debt specialization
A new insight to optimal capital structure
title Core predictors of debt specialization
spellingShingle Core predictors of debt specialization
Khan, Kanwal Iqbal
Agency conflicts
Corporate financial strategy
Debt specialization
Financial modeling
Information asymmetry
Optimal debt structure
Risk management
Transaction cost
Mathematics(all)
title_short Core predictors of debt specialization
title_full Core predictors of debt specialization
title_fullStr Core predictors of debt specialization
title_full_unstemmed Core predictors of debt specialization
title_sort Core predictors of debt specialization
author Khan, Kanwal Iqbal
author_facet Khan, Kanwal Iqbal
Qadeer, Faisal
Mata, Mário Nuno
Chavaglia Neto, José
Sabir, Qurat Ul An
Martins, Jéssica Nunes
Filipe, José António
author_role author
author2 Qadeer, Faisal
Mata, Mário Nuno
Chavaglia Neto, José
Sabir, Qurat Ul An
Martins, Jéssica Nunes
Filipe, José António
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Khan, Kanwal Iqbal
Qadeer, Faisal
Mata, Mário Nuno
Chavaglia Neto, José
Sabir, Qurat Ul An
Martins, Jéssica Nunes
Filipe, José António
dc.subject.por.fl_str_mv Agency conflicts
Corporate financial strategy
Debt specialization
Financial modeling
Information asymmetry
Optimal debt structure
Risk management
Transaction cost
Mathematics(all)
topic Agency conflicts
Corporate financial strategy
Debt specialization
Financial modeling
Information asymmetry
Optimal debt structure
Risk management
Transaction cost
Mathematics(all)
description Khan, K. I., Qadeer, F., Mata, M. N., Chavaglia Neto, J., Sabir, Q. U. A., Martins, J. N., & Filipe, J. A. (2021). Core predictors of debt specialization: A new insight to optimal capital structure. Mathematics, 9(9), 1-25. [975]. https://doi.org/10.3390/math9090975
publishDate 2021
dc.date.none.fl_str_mv 2021-05-25T00:58:01Z
2021-05-01
2021-05-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/118262
url http://hdl.handle.net/10362/118262
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2227-7390
PURE: 31443504
https://doi.org/10.3390/math9090975
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 25
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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