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Factors that Affect Credit Rating: An Application of Ordered Probit Models

Author

Listed:
  • Ken Hung

    (A.R. Sanchez School of Business, Texas A & M International University, Laredo, Texas, USA)

  • Hui Wen Cheng

    (Department of International Business, Ming Chuan University, Taipei, Taiwan, R. O. C.)

  • Shih-shen Chen

    (Department of International Business and Trade, Shu-Te University, Kaohsiung, Taiwan, R. O.C.)

  • Ying-Chen Huang

    (Department of Economics, National Chung Cheng University, Chia-yi, Taiwan, R. O. C.)

Abstract

Corporate credit ratings have become more important after the 2008 financial crisis. To explore the mystery, we employ the ordered probit regression models to examine the relationship between the credit rating and financial ratios in electric utilities, chemicals and communications equipment companies whose credits were rated by the S&P between 2006 and 2010 in North America. Consistent with prior research, we show that credit ratings are positively related to EBITDA interest coverage, return on assets and total assets while negatively related to debt ratio and cash to current liabilities ratio. Furthermore, we show that all the models over-predict the low rating categories while under-predict the high rating categories. The result of our model is among the best in terms of predictive power.

Suggested Citation

  • Ken Hung & Hui Wen Cheng & Shih-shen Chen & Ying-Chen Huang, 2013. "Factors that Affect Credit Rating: An Application of Ordered Probit Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 94-108, December.
  • Handle: RePEc:rjr:romjef:v::y:2013:i:4:p:94-108
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    References listed on IDEAS

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    2. Ala’a Adden Abuhommous & Ahmad Salim Alsaraireh & Huthaifa Alqaralleh, 2022. "The impact of working capital management on credit rating," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-20, December.
    3. Sen Guo & Huiru Zhao & Chunjie Li & Haoran Zhao & Bingkang Li, 2016. "Significant Factors Influencing Rural Residents’ Well-Being with Regard to Electricity Consumption: An Empirical Analysis in China," Sustainability, MDPI, vol. 8(11), pages 1-13, November.
    4. Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
    5. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
    6. Iulian Viorel Brasoveanu & Florin Dobre & Laura Brad, 2014. "Increasing Financial Audit Quality Using A New Model To Estimate Financial Performance," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 88-107, October.
    7. Jaspreet Kaur & Madhu Vij & Ajay Kumar Chauhan, 2023. "Signals influencing corporate credit ratings—a systematic literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(1), pages 91-114, March.
    8. Marat Z. Kurbangaleev & Victor A. Lapshin & Zinaida V. Seleznyova, 2018. "Studying The Replicability Of Aggregate External Credit Assessments Using Public Information," HSE Working papers WP BRP 71/FE/2018, National Research University Higher School of Economics.
    9. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.

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    More about this item

    Keywords

    ordered probit regression model; credit rating; financial ratios; 2008 financial crisis; Standard&Poor;
    All these keywords.

    JEL classification:

    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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