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.
All Science Journal Classification (ASJC) codes
- Economics, Econometrics and Finance(all)
- Business, Management and Accounting(all)