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Financial determinants of credit risk in the logistics and shipping industries

Author

Listed:
  • Su-Han Woo

    (Chung-Ang University)

  • Min-Su Kwon

    (Chung-Ang University)

  • Kum Fai Yuen

    (Nanyang Technological University)

Abstract

This study examines factors affecting the credit risk of global logistics and shipping companies using Altman’s Z-score and Ohlson’s O-score models. Panel data multiple regression analysis is conducted to evaluate the impacts of various financial ratios on credit risk for both industries and to assess their unique characteristics. We find that while credit risk is, on average, similar between the shipping and logistics industries, the variability in credit risk in the shipping industry is much higher. We also find that while both equity and current ratios have a significant impact on credit risk in both industries, return on assets and the quick ratio have the most significant impact on the logistics and maritime industries, respectively. There are slight differences in the determinants of credit risk when analyses are further segmented into different regions (i.e., Asia, EU, USA and Africa). This study introduces scientific models and recommends financial indicators for financiers to evaluate the credit risk of both industries, help improve decision-making, and minimize the probability of default by debtors.

Suggested Citation

  • Su-Han Woo & Min-Su Kwon & Kum Fai Yuen, 2021. "Financial determinants of credit risk in the logistics and shipping industries," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 268-290, June.
    • Handle: RePEc:pal:marecl:v:23:y:2021:i:2:d:10.1057_s41278-020-00157-4
    DOI: 10.1057/s41278-020-00157-4
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    1. Lown, Cara & Morgan, Donald P., 2006. "The Credit Cycle and the Business Cycle: New Findings Using the Loan Officer Opinion Survey," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(6), pages 1575-1597, September.
    2. Wolfgang Drobetz & Dirk Schilling & Lars Tegtmeier, 2010. "Common risk factors in the returns of shipping stocks," Maritime Policy & Management, Taylor & Francis Journals, vol. 37(2), pages 93-120, March.
    3. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    4. Williams, Gwion & Alsakka, Rasha & ap Gwilym, Owain, 2013. "The impact of sovereign rating actions on bank ratings in emerging markets," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 563-577.
    5. Jung-Suk Choi & Kang-Hyuk Kim & Hee-Jung Han, 2018. "Development of early warning index in Korean shipping industry by using signal approach," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(8), pages 1007-1020, November.
    6. Liang, Qi, 2003. "Corporate Financial Distress Diagnosis in China : Empirical Analysis Using Credit Scoring Models," Hitotsubashi Journal of commerce and management, Hitotsubashi University, vol. 38(1), pages 13-28, October.
    7. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    8. R. Midoro & E. Musso & F. Parola *, 2005. "Maritime liner shipping and the stevedoring industry: market structure and competition strategies," Maritime Policy & Management, Taylor & Francis Journals, vol. 32(2), pages 89-106, April.
    9. 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.
    10. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    11. 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.
    12. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    13. Malkiel, Burton G, 1995. "Returns from Investing in Equity Mutual Funds 1971 to 1991," Journal of Finance, American Finance Association, vol. 50(2), pages 549-572, June.
    14. Erasmus K. Kersting & Mark A. Wynne, 2009. "Trade, globalization and the financial crisis," Economic Letter, Federal Reserve Bank of Dallas, vol. 4(nov).
    15. Hyo-Won Kang & Grace W Y Wang & Hee-Seok Bang & Su-Han Woo, 2016. "Economic performance and corporate financial management of shipping firms," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 18(3), pages 317-330, September.
    16. Su-Han Woo & Po-Lin Lai & Ying-Hsiu Chen & Ching-Chiao Yang, 2019. "Meta-frontier function approach to operational efficiency for shipping companies," Maritime Policy & Management, Taylor & Francis Journals, vol. 46(5), pages 529-544, July.
    17. Grace W.Y. Wang & Su-Han Woo & Joan Mileski, 2014. "The relative efficiency and financial risk assessment of shipping companies," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(7), pages 651-666, December.
    18. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    19. Trevor D Heaver, 2002. "The Evolving Roles of Shipping Lines in International Logistics," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 4(3), pages 210-230, September.
    20. Lopez, Jose A. & Saidenberg, Marc R., 2000. "Evaluating credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 151-165, January.
    21. Ahmed A. El-Masry & Mojisola Olugbode & John Pointon, 2010. "The exposure of shipping firms’ stock returns to financial risks and oil prices: a global perspective," Maritime Policy & Management, Taylor & Francis Journals, vol. 37(5), pages 453-473, April.
    22. Manolis G. Kavussanos & Arne Juell-Skielse & Matthew Forrest, 2003. "International comparison of market risks across shipping-related industries," Maritime Policy & Management, Taylor & Francis Journals, vol. 30(2), pages 107-122, January.
    23. Reza Kazemi & Ali Mosleh, 2012. "Improving Default Risk Prediction Using Bayesian Model Uncertainty Techniques," Risk Analysis, John Wiley & Sons, vol. 32(11), pages 1888-1900, November.
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