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Appraising Credit Ratings: Does The Cap Fit Better Than The Roc?

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  • R. John Irwin
  • Timothy C. Irwin

Abstract

ROC and CAP analysis are alternative methods for evaluating a wide range of diagnostic systems, including assessments of credit risk. ROC analysis is widely used in many fields, but in finance CAP analysis is more common. We compare the two methods, using as an illustration the ability of the OECD’s country risk ratings to predict whether a country will have a program with the IMF (an indicator of financial distress). ROC and CAP analyses both have the advantage of generating measures of accuracy that are independent of the choice of diagnostic threshold, such as risk rating. ROC analysis has other beneficial features, including theories for fitting models to data and for setting the optimal threshold, that we show could also be incorporated into CAP analysis. But the natural interpretation of the ROC measure of accuracy and the independence of ROC curves from the probability of default are advantages unavailable to CAP analysis.
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Suggested Citation

  • R. John Irwin & Timothy C. Irwin, 2013. "Appraising Credit Ratings: Does The Cap Fit Better Than The Roc?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 18(4), pages 396-408, October.
    • Handle: RePEc:wly:ijfiec:v:18:y:2013:i:4:p:396-408
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    1. Das, Sanjiv R. & Hanouna, Paul & Sarin, Atulya, 2009. "Accounting-based versus market-based cross-sectional models of CDS spreads," Journal of Banking & Finance, Elsevier, vol. 33(4), pages 719-730, April.
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    Cited by:

    1. Turalay Kenc & Emrah Ismail Cevik & Sel Dibooglu, 2021. "Bank default indicators with volatility clustering," Annals of Finance, Springer, vol. 17(1), pages 127-151, March.
    2. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    3. Renatas Špicas & Airidas Neifaltas & Rasa Kanapickienė & Greta Keliuotytė-Staniulėnienė & Deimantė Vasiliauskaitė, 2023. "Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform," Risks, MDPI, vol. 11(7), pages 1-30, July.
    4. McCarthy, Patrick, 2024. "Predicting trips to health care facilities: A binary logit and receiver operating characteristics (ROC) approach," Research in Transportation Economics, Elsevier, vol. 103(C).
    5. Péter Bauer & Marianna Endrész, 2016. "Modelling Bankruptcy Using Hungarian Firm-Level Data," MNB Occasional Papers 2016/122, Magyar Nemzeti Bank (Central Bank of Hungary).
    6. Nehrebecka Natalia, 2018. "An Evaluation of the Discriminatory Power of Selected Polish Bankruptcy Prediction Models As Part of the Validation Process," Financial Sciences. Nauki o Finansach, Sciendo, vol. 23(4), pages 63-88, December.
    7. Suleiman A. Badayi & Bolaji T. Matemilola & Bany‐Ariffin A.N & Lau Wei Theng, 2021. "Does corporate social responsibility influence firm probability of default?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3377-3395, July.
    8. Pranith K. Roy & Krishnendu Shaw, 2023. "A credit scoring model for SMEs using AHP and TOPSIS," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 372-391, January.

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