Copyright © 2005 Elsevier B.V. All rights reserved.
Computing, Artificial Intelligence and Information Management
Received 22 July 2004;
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Abstract
In order to evaluate the creditworthiness of various countries, a learning model is induced from the 1998 Standard and Poor’s country risk ratings, using the 1998 values of nine economic and three political indicators. This learning model allows the construction of a partially ordered set describing the relative superiority of countries on the basis of their creditworthiness, and it is shown that the Condorcet linear extensions of this poset match closely the S&P ratings. Moreover, the ratings derived from the model correlate highly with those of other rating agencies. The model is shown to provide excellent ratings even when applied to the following years’ data or to the ratings of previously unrated countries. Rating changes implemented by S&P in subsequent years resolved most of the (few) discrepancies between the constructed poset and S&P’s initial ratings.
Keywords: Data mining; LAD; Country risk ratings; Partially ordered set; Cross-validation
Article Outline
- 1. Introduction
- 2. Data
- 2.1. Data set
- 3. Logical analysis of data—An overview
- 4. Combinatorial model
- 4.1. From pairwise country comparisons to pseudo-observations
- 4.2. From pseudo-observations to relative preferences
- 4.3. Classification of pseudo-observations and cross-validation
- 4.4. From LAD relative preferences to a partial order on the set of countries
- 4.5. Extending partially ordered sets to “extreme” linear preorders
- 5. Model analysis
- 5.1. Canonical relative preferences
- 5.2. Logical dominance relationship
- 5.2.1. Discrepancies with S&P
- 5.3. Optimistic and pessimistic extensions
- 6. Temporal validity
- 6.1. Relative preferences
- 6.2. Logical dominance relationship
- 6.2.1. Discrepancies with S&P
- 6.3. Optimistic and pessimistic extensions
- 7. Predicting creditworthiness of unrated countries
- 8. Concluding remarks
- Appendix A. Appendix
- References






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