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

  • 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.)

Registered author(s):

    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.

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    Article provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.

    Volume (Year): (2013)
    Issue (Month): 4 (December)
    Pages: 94-108

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    Handle: RePEc:rjr:romjef:v::y:2013:i:4:p:94-108
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