The use of effect sizes in credit rating models
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Authors
Steyn, Hendrik Stefanus
Issue Date
2014-12
Type
Dissertation
Language
en
Keywords
Practical significance , Logistic regression , Cohen‟s d , Probability of default , Effect size , Goodness-of-fit , Odds ratio , Area under the curve , Multi-collinearity , Basel II
Alternative Title
Abstract
The aim of this thesis was to investigate the use of effect sizes to report the results of
statistical credit rating models in a more practical way. Rating systems in the form of
statistical probability models like logistic regression models are used to forecast the
behaviour of clients and guide business in rating clients as “high” or “low” risk borrowers.
Therefore, model results were reported in terms of statistical significance as well as business
language (practical significance), which business experts can understand and interpret. In this
thesis, statistical results were expressed as effect sizes like Cohen‟s d that puts the results into
standardised and measurable units, which can be reported practically. These effect sizes
indicated strength of correlations between variables, contribution of variables to the odds of
defaulting, the overall goodness-of-fit of the models and the models‟ discriminating ability
between high and low risk customers.
Description
Citation
Steyn, Hendrik Stefanus (2014) The use of effect sizes in credit rating models, University of South Africa, Pretoria, <http://hdl.handle.net/10500/18790>