Skip to main content
SpringerLink
Log in

Combinatorial Methods for Constructing Credit Risk Ratings

  • Reference work entry
  • First Online:
Handbook of Financial Econometrics and Statistics

  • 9207 Accesses

Abstract

This study uses a novel method, the Logical Analysis of Data (LAD), to reverse engineer and construct credit risk ratings which represent the creditworthiness of financial institutions and countries. LAD is a data mining method based on combinatorics, optimization, and Boolean logic that utilizes combinatorial search techniques to discover various combinations of attribute values that are characteristic of the positive or negative character of observations. The proposed methodology is applicable in the general case of inferring an objective rating system from archival data, given that the rated objects are characterized by vectors of attributes taking numerical or ordinal values. The proposed approaches are shown to generate transparent, consistent, self-contained, and predictive credit risk rating models, closely approximating the risk ratings provided by some of the major rating agencies. The scope of applicability of the proposed method extends beyond the rating problems discussed in this study and can be used in many other contexts where ratings are relevant.

We use multiple linear regression to derive the logical rating scores.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 849.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Wall Street Letter. 2006. CFA To Senate: Follow Our Lead On Credit Rating.

  2. 2.

    The Economist, July 15, 1995, 62.

  3. 3.

    The presentation in this section is partially based on Hammer et al. (2006).

  4. 4.

    The presentation in this section is based on Hammer et al. (2012).

  5. 5.

    The presentation in this section is based on Hammer et al. (2006, 2012).

References

  • Afonso, A. (2003). Understanding the determinants of sovereign debt ratings: Evidence for the two leading agencies. Journal of Economics and Finance, 27(1), 56–74.

    Article  Google Scholar 

  • Afonso, A., Gomes, P., & Rother, P. (2007). What “hides” behind sovereign debt ratings? (European Central Bank Working Paper Series 711). https://www.repository.utl.pt/bitstream/10400.5/2134/1/ecbwp711.pdf

  • Alexe, S. (2002). Datascope – A new tool for logical analysis of data. DIMACS Mixer Series. DIMACS, Rutgers University.

    Google Scholar 

  • Alexe, G., Alexe, S., Bonates, T. O., & Kogan, A. (2007). Logical analysis of data – The vision of Peter L. Hammer. Annals of Mathematics and Artificial Intelligence, 49, 265–312.

    Article  Google Scholar 

  • Altman, E. I., & Rijken, H. A. (2004). How rating agencies achieve rating stability. Journal of Banking and Finance, 28, 2679–2714.

    Article  Google Scholar 

  • Barr, R. S., & Siems, T. F. (1994). Predicting bank failure using DEA to quantify management quality. Federal Reserve Bank of Dallas Financial Industry Studies, 1, 1–31.

    Google Scholar 

  • Basel Committee on Banking Supervision. (2001). The internal ratings based approach. http://www.bis.org/publ/bcbsca05.pdf

  • Basel Committee on Banking Supervision. (2004). Bank failures in mature economies (Working Paper 13).

    Google Scholar 

  • Basel Committee on Banking Supervision. (2006). International convergence of capital measurement and capital standards: A revised framework (Basel II).

    Google Scholar 

  • Bhatia, A.V. (2002). Sovereign credit risk ratings: An evaluation (IMF Working Paper WP/03/170).

    Google Scholar 

  • Boros, E., Hammer, P. L., Ibaraki, T., & Kogan, A. (1997). Logical analysis of numerical data. Mathematical Programming, 79, 163–190.

    Google Scholar 

  • Boros, E., Hammer, P. L., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. (2000). An implementation of logical analysis of data. IEEE Transactions on Knowledge and Data Engineering, 12(2), 292–306.

    Article  Google Scholar 

  • Bouchet, M. H., Clark, E., & Groslambert, B. (2003). Country risk assessment: A guide to global investment strategy. Chichester: Wiley.

    Google Scholar 

  • Bourke, P., & Shanmugam, B. (1990). An introduction to bank lending (Addison-Wesley Business Series). Sydney: Addison-Wesley.

    Google Scholar 

  • Brewer, T. L., & Rivoli, P. (1990). Politics and perceived country creditworthiness in international banking. Journal of Money, Credit and Banking, 22, 357–369.

    Article  Google Scholar 

  • Brewer, T. L., & Rivoli, P. (1997). Political instability and country risk. Global Finance Journal, 8(2), 309–321.

    Article  Google Scholar 

  • Cantor, R., & Packer, F. (1996). Determinants and impact of sovereign credit ratings. FRBNY Economic Policy Review, 2, 37–53.

    Google Scholar 

  • Choo E.U., & Wedley W.C., (2004). A Common Framework for Deriving Preference Values from Pairwise Comparison Matrices. Computers and Operational Research 31, 893–908.

    Article  Google Scholar 

  • Citron, J. T., & Neckelburg, G. (1987). Country risk and political instability. Journal of Development Economics, 25, 385–395.

    Article  Google Scholar 

  • Crama, Y., Hammer, P. L., & Ibaraki, T. (1988). Cause-effect relationships and partially defined boolean functions. Annals of Operations Research, 16, 299–326.

    Article  Google Scholar 

  • Curry, T., & Shibut, L. (2000). The Cost of the Savings and Loan Crisis: Truth and Consequences. FDIC Banking Review, 2(2), 26–35.

    Google Scholar 

  • Czyzyk, J., Mesnier, M. P., & Moré, J. J. (1998). The NEOS server. IEEE Computer Science Engineering, 5(3), 68–75.

    Article  Google Scholar 

  • de Servigny, A., & Renault, O. (2004). Measuring and managing credit risk. New York: McGraw-Hill.

    Google Scholar 

  • Eliasson, A. (2002). Sovereign credit ratings (Working Papers 02-1). Deutsche Bank.

    Google Scholar 

  • Ferri, G., Liu, L.-G., & Stiglitz, J. (1999). The procyclical role of rating agencies: Evidence from the east asian crisis. Economic Notes, 3, 335–355.

    Article  Google Scholar 

  • Fitch Ratings. (2001). Fitch simplifies bank rating scales. Technical Report.

    Google Scholar 

  • Fitch Ratings. (2006). The role of support and joint probability analysis in bank ratings. Fitch Special Report.

    Google Scholar 

  • Galindo, J., & Tamayo, P. (2000). Credit risk assessment using statistical and machine learning/basic methodology and risk modeling applications. Computational Economics, 15, 107–143.

    Article  Google Scholar 

  • Gehrlein, W. V., & Lepelley, D. (1998). The condorcet efficiency of approval voting and the probability of electing the condorcet loser. Journal of Mathematical Economics, 29, 271–283.

    Article  Google Scholar 

  • Hammer, P. L. (1986). Partially defined boolean functions and cause-effect relationships. International conference on multi-attribute decision making via OR-based expert systems. Passau: University of Passau.

    Google Scholar 

  • Hammer, P. L., Kogan, A., & Lejeune, M. A. (2006). Modeling country risk ratings using partial orders. European Journal of Operational Research, 175(2), 836–859.

    Article  Google Scholar 

  • Hammer, P. L., Kogan, A., & Lejeune, M. A. (2011). Reverse-engineering country risk ratings: Combinatorial non-recursive model. Annals of Operations Research, 188, 185–213.

    Article  Google Scholar 

  • Hammer, P. L., Kogan, A., & Lejeune, M. A. (2012). A logical analysis of banks’ financial strength ratings. Expert Systems with Applications, 39(9), 7808–7821.

    Article  Google Scholar 

  • Haque, N. U., Kumar, M. S., Mark, N., & Mathieson, D. (1996). The economic content of indicators of developing country creditworthiness. International Monetary Fund Working Paper, 43(4), 688–724.

    Article  Google Scholar 

  • Haque, N. U., Kumar, M. S., Mark, N., & Mathieson, D. (1998). The relative importance of political and economic variables in creditworthiness ratings. International Monetary Fund Working Paper, 46, 1–13.

    Google Scholar 

  • Hu, Y.-T., Kiesel, R., & Perraudin, W. (2002). The estimation of transition matrices for sovereign credit ratings. Journal of Banking and Finance, 26(7), 1383–1406.

    Article  Google Scholar 

  • Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37, 543–558.

    Article  Google Scholar 

  • Jain, K., Duin, R., & Mayo, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 4–37.

    Article  Google Scholar 

  • Kaminsky, G., & Schmukler, S. L. (2002). Emerging market instability: Do sovereign ratings affect country risk and stock returns? World Bank Economic Review, 16, 171–195.

    Article  Google Scholar 

  • Kaufmann, D., Kraay A., & Zoido-Lobaton, P. (1999a). Aggregating governance indicators. (World Bank Policy Research Department Working Paper, 2195).

    Google Scholar 

  • Kaufmann, D., Kraay, A., & Zoido-Lobaton, P. (1999b). Governance matters (World Bank Policy Research Department Working Paper, 2196).

    Google Scholar 

  • Kunczik, M. (2001). Globalization: News media, images of nations and the flow of international capital with special reference to the role of rating agencies. Paper presented at the IAMCR Conference, Singapore, pp. 1–49.

    Google Scholar 

  • Larrain, G., Reisen, H., & von Maltzan, J. (1997). Emerging market risk and sovereign credit ratings (pp. 1–30). OECD Development Centre Working Papers 124, OECD Publishing.

    Google Scholar 

  • Moody’s. (2006). Bank financial strength ratings: Revised methodology. Moody’s Global Credit Research Report.

    Google Scholar 

  • Mora, N. (2006). Sovereign credit ratings: Guilty beyond reasonable doubt? Journal of Banking and Finance, 30(7), 2041–2062.

    Article  Google Scholar 

  • Morgan, D. P. (2002). Rating banks: Risk and uncertainty in an opaque industry. The American Economic Review, 92(4), 874–888.

    Article  Google Scholar 

  • Ng, W.-Y., Choi, K.-W., & Shum, K.-H. (1996). Arbitrated matching: Formulation and protocol. European Journal of Operational Research, 88(2), 348–357.

    Article  Google Scholar 

  • Poon, W. P. H., Firth, M., & Fung, H.-G. (1999). A multivariate analysis of the determinants of Moody’s bank financial strength ratings. Journal of International Financial Markets, Institutions & Money, 9(3), 267–283.

    Article  Google Scholar 

  • Quenouille, M. (1949). Approximate tests of correlation in time series. Journal of the Royal Statistical Society, Series B, 11, 18–84.

    Google Scholar 

  • Reinhart, C. M. (2002). Default, currency crises, and sovereign credit ratings. World Bank Economic Review, 16, 151–170.

    Article  Google Scholar 

  • Sarkar, S., & Sriram, R. S. (2001). Bayesian models for early warning of bank failures. Management Science, 47(11), 1457–1475.

    Article  Google Scholar 

  • Sobehart, J. R., Keenan, S. C., & Stein, R. M. (2000). Benchmarking quantitative default risk models: A validation methodology. New York: Moody’s Investors Service.

    Google Scholar 

  • Stein, R. M. (2002). Benchmarking default prediction models: Pitfalls and remedies in model validation. New York: Moody’s KMV.

    Google Scholar 

  • Sy, A. N. R. (2004). Rating the rating agencies: Anticipating currency crises or debt crises? Journal of Banking and Finance, 28(11), 2845–2867.

    Article  Google Scholar 

  • Tarjan, R. E. (1972). Depth-first search and linear graph algorithms. SIAM Journal on Computing, 1, 146–160.

    Article  Google Scholar 

  • Treacy, W. F., & Carey, M. S. (2000). Credit risk rating systems at large US banks. Journal of Banking & Finance, 24(1–2), 167–201.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Rutgers Business School, Rutgers, The State University of New Jersey, Newark–New Brunswick, NJ, USA

    Alexander Kogan

  2. Rutgers Center for Operations Research (RUTCOR), Piscataway, NJ, USA

    Alexander Kogan

  3. George Washington University, Washington, DC, USA

    Miguel A. Lejeune

Authors
  1. Alexander Kogan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel A. Lejeune
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Kogan .

Editor information

Editors and Affiliations

  1. Department of Finance and Economics, Rutgers Business School, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA

    Cheng-Few Lee

  2. Center for PBBEF Research, North Brunswick, New Jersey, USA

    John C. Lee

Appendix

Appendix

See Tables 16.15 and 16.16.

Table 16.15 Standard & Poor’s country rating system
Full size table
Table 16.16 1998 Ratings
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

Kogan, A., Lejeune, M.A. (2015). Combinatorial Methods for Constructing Credit Risk Ratings. In: Lee, CF., Lee, J. (eds) Handbook of Financial Econometrics and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7750-1_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7750-1_16

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7749-5

  • Online ISBN: 978-1-4614-7750-1

  • eBook Packages: Business and Economics

Publish with us

Policies and ethics

Search

Navigation