Core predictors of debt specialization:a new insight to optimal capital structure
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.15/3466 |
Resumo: | Debt 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. |
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Core predictors of debt specialization:a new insight to optimal capital structureDebt specializationCorporate financial strategyOptimal debt structureAgency conflictsTransaction costInformation asymmetryFinancial modelingRisk managementDebt 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.MDPIRepositório Científico do Instituto Politécnico de SantarémKhan, Kanwal IqbalQadeer, FaisalMata, Mário NunoChavaglia Neto, JoséSabir, Qurat ul AnMartins, Jéssica NunesFilipe, José António2021-04-29T13:43:57Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.15/3466engKhan, 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/math90909752227-739010.3390/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:RCAAP2024-01-21T07:35:17Zoai:repositorio.ipsantarem.pt:10400.15/3466Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:55:14.546900Repositório 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:a new insight to optimal capital structure |
spellingShingle |
Core predictors of debt specialization:a new insight to optimal capital structure Khan, Kanwal Iqbal Debt specialization Corporate financial strategy Optimal debt structure Agency conflicts Transaction cost Information asymmetry Financial modeling Risk management |
title_short |
Core predictors of debt specialization:a new insight to optimal capital structure |
title_full |
Core predictors of debt specialization:a new insight to optimal capital structure |
title_fullStr |
Core predictors of debt specialization:a new insight to optimal capital structure |
title_full_unstemmed |
Core predictors of debt specialization:a new insight to optimal capital structure |
title_sort |
Core predictors of debt specialization:a new insight to optimal capital structure |
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 |
Repositório Científico do Instituto Politécnico de Santarém |
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 |
Debt specialization Corporate financial strategy Optimal debt structure Agency conflicts Transaction cost Information asymmetry Financial modeling Risk management |
topic |
Debt specialization Corporate financial strategy Optimal debt structure Agency conflicts Transaction cost Information asymmetry Financial modeling Risk management |
description |
Debt 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-04-29T13:43:57Z 2021 2021-01-01T00:00:00Z |
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/10400.15/3466 |
url |
http://hdl.handle.net/10400.15/3466 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Khan, 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/math9090975 2227-7390 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 |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
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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1799137041743609856 |