Measuring the Discriminative Power of Rating Systems

By B. Engelmann, E. Hayden, Dirk Tasche. Social Science Research Network, 2003

131 citations recorded by the source corpus.

Assessing the discriminative power of rating systems is an important question to banks and to regulators. In this article we analyze the Cumulative Accuracy Profile (CAP) and the Receiver Operating Characteristic (ROC) which are both commonly used in practice. We give a test-theoretic interpretation for the concavity of the CAP and the ROC curve and demonstrate how this observation can be used for more efficiently exploiting the informational contents of accounting ratios. Furthermore, we show that two popular summary statistics of these concepts, namely the Accuracy Ratio and the area under the ROC curve, contain the same information and we analyse the statistical properties of these measures. We show in detail how to identify accounting ratios with high discriminative power, how to calculate confidence intervals for the area below the ROC curve, and how to test if two rating models validated on the same data set are different. All concepts are illustrated by applications to real data.

Subfields: Accounting, Artificial Intelligence, Finance

View Paper: https://www.semanticscholar.org/paper/07a47bae03a3c6c25bf20896b2c16a768a35ad6e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Edward I. Altman
2. G. Sabato
3. Francesco Ciampi
4. N. Wilson
5. Hadiye Aslan
6. Praveen Kumar

In Artificial Intelligence

1. Edward I. Altman
2. G. Sabato
3. Praveen Kumar
4. Hadiye Aslan

In Finance

1. G. Sabato
2. Edward I. Altman
3. Francesco Ciampi
4. N. Wilson
5. Hadiye Aslan
6. Praveen Kumar

Citations in the corpus

• Support Vector Machines (SVM) as a Technique for Solvency Analysis (2008, unknown): “i i T b x w y C w w v b w L ξ ν ξ α ξ α ξ (5)”
• MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET (2013, background): “The second index is called the accuracy ratio (AR) and is defined as the ratio of the area between the cumulative accuracy profile (CAP) of the rating model being validated and the CAP of the random model, and the area between the CAP of the perfect rating model and the CAP of the random model (see ”
• Mitigating the pro-cyclicality of basel II (2010, unknown): “For references on these performance statistics see, for example, Sobehart et al. (2000), Sobehart and Keenan (2001), and Engelmann et al. (2003).”