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Core Predictors of Debt Specialization: A New Insight to Optimal Capital Structure

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Listed:
  • Kanwal Iqbal Khan

    (Institute of Business & Management, University of Engineering and Technology, Lahore 54000, Pakistan)

  • Faisal Qadeer

    (Lahore Business School, The University of Lahore, Lahore 54000, Pakistan)

  • Mário Nuno Mata

    (ISCAL—Instituto Superior de Contabilidade e Administração de Lisboa, Instituto Politécnico de Lisboa, Avenida Miguel Bombarda 20, 1069-035 Lisboa, Portugal
    Polytechnic Institute of Santarém, School of Management and Technology (ESGTS-IPS), 2001-904 Santarém, Portugal)

  • José Chavaglia Neto

    (Fundação Getúlio Vargas (EESP-FGV), Itapeva Street, São Paulo 01332-000, Brazil)

  • Qurat ul An Sabir

    (School of Statistics, Minhaj University Lahore, Lahore 54000, Pakistan
    Biofluids and Biosystems Modelling Lab (BBML), Department of Engineering, Faculty of Agriculture, Dalhousie University, Halifax, NS B3H 4R2, Canada)

  • Jéssica Nunes Martins

    (NOVA Information Management School, (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
    BPI—Banco Portugês de Investimento—CaixaBank Group, Accounting Department, Largo Jean Monnet 1 9º andar, 1269-067 Lisboa, Portugal)

  • José António Filipe

    (Departamento de Matemática, Iscte—Instituto Universitário de Lisboa, ISTAR-Iscte, BRU-Iscte, 1649-026 Lisboa, Portugal)

Abstract

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.

Suggested Citation

  • Kanwal Iqbal Khan & Faisal Qadeer & Mário Nuno Mata & José Chavaglia Neto & Qurat ul An Sabir & Jéssica Nunes Martins & José António Filipe, 2021. "Core Predictors of Debt Specialization: A New Insight to Optimal Capital Structure," Mathematics, MDPI, vol. 9(9), pages 1-25, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:975-:d:544265
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