We compared the ability of various empirical methods to reproduce public credit ratings (PCRs) of industrial companies (ICs) from BRICS countries using publicly available information. This task is of primary importance for researchers and practitioners as a lot of BRICS’ ICs lack public credit ratings (CRs) from reputable rating agencies such as Moody’s, Standard and Poor’s or Fitch. The paper is aimed at filling the gap in the existing research as only very few efforts were focused on prediction of PCRs of ICs from entire BRICS IC community. The modelled variables are CRs of 208 BRICS’ industrial companies assigned by Moody’s at the year-end from 2006 till 2016. The sample included 1217 observations. Financial dependent variables included companies’ revenue, operating profitability, interest coverage ratio, debt/book capitalization and cash flow debt coverage. Non-financial dependent variables included dummies for home region, industry, affiliation with the state and a set of macroeconomic data of IC’s home countries. The set of statistical methods included linear discriminant analysis (LDA), ordered logit regression (OLR), support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The resulting models were checked for in-sample and out-of-sample predictive fit. Our findings revealed that among considered methods artificial intelligence models (AI) – SVM, ANN and RF outperformed LDA and OLR by predictive power. On testing sample, AI gave on average 55% of precise results and up to 99% with an error within one rating grade; RF demonstrated the best outcome (58% and 100%). Conversely, LDA and OLR on average gave only 37% of precise results and up to 70% with an error within one grade. LDA and OLR also gave higher share of Type I errors (overestimation of ratings) than that of AI. Therefore, AI should have higher practical application than DA and OLR for predicting the ratings of BRICS ICs.
We compare several models for estimating the default probabilities of Russian banks using national statistics from 1998 to 2011, and find that a binary logit regression with a quasi-panel data structure works best. We conclude that there is a quadratic U-shaped relationship between a bank's capital adequacy ratio and its probability of default. In addition, macroeconomic, institutional, and time factors significantly improve model accuracy. These results are useful for the national financial regulatory authorities, as well as for risk-managers in commercial banks.