Using Financial Ratios and Regression to Estimate Credit Risk
Summary
The document outlines how to develop credit scores or estimate corporate default probability from financial statements. It recommends beginning with Altman’s scoring model and reviewing comparative research on scoring approaches. A typical workflow selects informative financial ratios and uses them as predictors in a regression model; suggested probability models include linear probability, logit, and probit regression.
The discussion cautions that ratio importance and coefficients can change with economic conditions and industry, so a single fixed specification may not generalize. It also points to models that combine financial ratios with industry and macroeconomic variables, and notes that leverage may have a nonlinear relationship with default risk. These are methodological pointers rather than a complete implementation guide: the document does not provide a dataset, validation results, or detailed criteria for selecting and evaluating predictors. Readers are directed toward foundational and comparative academic work for further development.
Key ideas
- Altman’s scoring framework is presented as a starting point for ratio-based credit analysis.
- Financial ratios can be selected as predictors and incorporated into regression models.
- Linear probability, logit, and probit approaches can be used to estimate default probability.
- Ratio effects may vary by industry and economic conditions.
- Default models may combine financial ratios with industry or macroeconomic variables, and some effects can be nonlinear.
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# Using Financial Ratios to get credit rating or PD # Using Financial Ratios to get credit rating or PD Hello I'm looking for papers, aside from ones that use CDS spreads, about credit rating development or estimating default probability based on financial ratios that also include methodology and maybe good/bad criteria? I think the methodology should be something that identifies which financial ratios are important given some financial statements and several computed financial ratios and then uses the important financial ratios to make a regression model. ## Answer by Quantopik (score 3, accepted) https://quant.stackexchange.com/a/16143 I suggest you to start from the Altman's model, that is the basic model to implement the kind of econometric analysis you're looking for. You can find the original paper at my Dropbox public folder. After that reading, you can find a number of paper about scoring models on SSRN or Google Scholar. Moreover, I suggest you to look for all academic papers that provide a comparison/overview among all scoring models, in order to have a complete view about this topic. As regards, your idea about the methodology used in this kind of models, it is generally correct, even if the financial ratios usually do not have the same weight/ beta coefficient in forecasting the companies will go broke, because of the fact they change over time according to the state of the economy (recession/growth), companies industry, etc. Anyway the methodology that is used is more or less always the same and similar to the one used in the Altman's paper and only changes the number and the type of the independent variables. If you need to estimate directly the default probability you can use the LPM, Logit/Probit regression, and, here you can find a good reference about this topic. Since it is not the basic model about default probability estimation using Logit/Probit model, I suggest you to read the main papers in the references and after carry on. Anyway, It is a nice paper about the topic because of it is based on a lot of indipendent variables (not only financial ratios) that can help you to make an idea about - which variables can be relevant and which not; - which variables have a linear effect, as assumed in hp and which ones, as, for instance, the leverage has a non-linear effect. ## Answer by user158037 (score 6) https://quant.stackexchange.com/a/16142 This is what Moody's does to calculate default probabilities, but I don't believe they give a whole lot of detail on their exact methodology because they sell their models as software. I quickly found this which gives a brief overview: http://www.moodysanalytics.com/~/media/Brochures/Enterprise-Risk-Solutions/RiskCalc/RiskCalcPlus-Fact-Sheet.ashx Edit- Also found this, goes into somewhat more detail. They use financial ratios as well as industry and economic factors as regressors in a probit model for default probability. https://riskcalc.moodysrms.com/us/research/docs/Americas/RiskCalc_v3_1_US.pdf
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