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Applying recurrent event analysis to understand the causes of changes in firm credit ratings

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  • Yan-Shing Chen
  • Po-Hsin Ho
  • Chih-Yung Lin
  • Wei-Che Tsai

Abstract

This study applies recurrent event analysis to examine the determinants of changes in firm credit ratings. This study uses two extended Cox proportional hazard models to examine upgrade and downgrade data separately. Explanatory variables are taken from financial ratios in Z-score (Altman, 1968) and AR-score (Altman and Rijken, 2004) models. The empirical results first suggest that sales to asset ratio and market equity to book debt ratio are the key explanatory variables for the sample comprising credit rating upgrade firms examined using Z-scores specification. Next, the sample of credit rating upgrade firms examined using AR-score variables reveals that the first rating of young firms is generally underestimated. Additionally, analysis of sample comprising credit downgrade firms examined using Z-score specification identifies working capital to asset ratio and market equity to book debt ratio as the key explicative variables. Furthermore, analysis of sample of credit downgrade firms examined using AR-score model reveals that larger firms are not easily downgraded, and old firms are more likely to be downgraded because of their ratings typically having initially been overestimated. Finally, high q firms with high retained earnings may suffer from underinvestment problem. Consequently, credit agencies may be reluctant to upgrade such firms.

Suggested Citation

  • Yan-Shing Chen & Po-Hsin Ho & Chih-Yung Lin & Wei-Che Tsai, 2012. "Applying recurrent event analysis to understand the causes of changes in firm credit ratings," Applied Financial Economics, Taylor & Francis Journals, vol. 22(12), pages 977-988, June.
  • Handle: RePEc:taf:apfiec:v:22:y:2012:i:12:p:977-988
    DOI: 10.1080/09603107.2011.633888
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    File URL: http://hdl.handle.net/10.1080/09603107.2011.633888
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    1. Samuli Ripatti & Juni Palmgren, 2000. "Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood," Biometrics, The International Biometric Society, vol. 56(4), pages 1016-1022, December.
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