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Empirics of Korean Shipping Companies’ Default Predictions

Author

Listed:
  • Sunghwa Park

    (Shipping Finance Research Division, Korea Maritime Institute, Busan 49111, Korea)

  • Hyunsok Kim

    (College of Economics and International Trade, Pusan National University, Busan 46241, Korea)

  • Janghan Kwon

    (Ocean Economy and Statistics Research Department, Korea Maritime Institute, Busan 49111, Korea)

  • Taeil Kim

    (Shipping and Logistics Research Department, Korea Maritime Institute, Busan 49111, Korea)

Abstract

In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this study can benefit both policymakers and market participants.

Suggested Citation

  • Sunghwa Park & Hyunsok Kim & Janghan Kwon & Taeil Kim, 2021. "Empirics of Korean Shipping Companies’ Default Predictions," Risks, MDPI, vol. 9(9), pages 1-17, September.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:9:p:159-:d:627098
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    References listed on IDEAS

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    1. Kavussanos, Manolis G. & Tsouknidis, Dimitris A., 2016. "Default risk drivers in shipping bank loans," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 71-94.
    2. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    3. Cesa-Bianchi, Ambrogio & Eguren Martin, Fernando & Thwaites, Gregory, 2019. "Foreign booms, domestic busts: The global dimension of banking crises," Journal of Financial Intermediation, Elsevier, vol. 37(C), pages 58-74.
    4. Maria Kovacova & Tomas Kliestik & Katarina Valaskova & Pavol Durana & Zuzana Juhaszova, 2019. "Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 10(4), pages 743-772, December.
    5. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    6. Altman, Edward I, 1971. "Railroad Bankruptcy Propensity," Journal of Finance, American Finance Association, vol. 26(2), pages 333-345, May.
    7. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    8. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    9. Jane Haider & Zhirong Ou & Stephen Pettit, 2019. "Predicting corporate failure for listed shipping companies," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 415-438, September.
    10. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    11. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    12. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    13. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    14. Katarina Valaskova & Pavol Durana & Peter Adamko & Jaroslav Jaros, 2020. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities," JRFM, MDPI, vol. 13(5), pages 1-16, May.
    15. Bussiere, Matthieu & Fratzscher, Marcel, 2006. "Towards a new early warning system of financial crises," Journal of International Money and Finance, Elsevier, vol. 25(6), pages 953-973, October.
    16. Grammenos, C.Th. & Nomikos, N.K. & Papapostolou, N.C., 2008. "Estimating the probability of default for shipping high yield bond issues," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(6), pages 1123-1138, November.
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    Cited by:

    1. Jeongmin Lee & Jinwoo Lee & Changhee Lee & Yulseong Kim, 2023. "Identifying ESG Trends of International Container Shipping Companies Using Semantic Network Analysis and Multiple Case Theory," Sustainability, MDPI, vol. 15(12), pages 1-20, June.

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