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Determinants of the probability of default: the case of the internationally listed shipping corporations

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  • Agata Lozinskaia
  • Andreas Merikas
  • Anna Merika
  • Henry Penikas

Abstract

In this study, we use a sample of 192 listed shipping companies and employ a logit model in order to investigate the determinants of the probability of default. We enhance our analysis by isolating not only the cases of company liquidations but also those cases where companies had to change their legal status due to warning liquidity signals. Our key findings are in line with prior research and moreover we depict a changing trend in the marginal effects of relevant variables, on the probability of default. We further show, through an empirical application, how the obtained results can be used in a managerial decision-making process and in a bank credit underwriting process in order to assess the creditworthiness of a shipping company.

Suggested Citation

  • Agata Lozinskaia & Andreas Merikas & Anna Merika & Henry Penikas, 2017. "Determinants of the probability of default: the case of the internationally listed shipping corporations," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(7), pages 837-858, October.
    • Handle: RePEc:taf:marpmg:v:44:y:2017:i:7:p:837-858
    DOI: 10.1080/03088839.2017.1345018
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    Cited by:

    1. Alexandridis, George & Kavussanos, Manolis G. & Kim, Chi Y. & Tsouknidis, Dimitris A. & Visvikis, Ilias D., 2018. "A survey of shipping finance research: Setting the future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 164-212.
    2. Rita Bužinskienė & Vera Gelashvili, 2024. "The Economic and Financial Health of Lithuanian Logistics Companies," Risks, MDPI, vol. 12(6), pages 1-19, June.
    3. Anna Burova & Henry Penikas & Svetlana Popova, 2021. "Probability of Default Model to Estimate Ex Ante Credit Risk," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 49-72, September.
    4. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.
    5. Michal Pavlicko & Jaroslav Mazanec, 2022. "Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group," Mathematics, MDPI, vol. 10(8), pages 1-22, April.

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