Novel Approaches on Sovereign Credit Ratings
Keywords:Artificial neural networks, Multilayer perceptron, Adaptive filters, Normalized least mean square algorithm, Sovereign credit ratings, Stock market prediction
Credit ratings that are transparent, impartial and reliable as well as being up to date, quickly and easily calculated will provide convenience to investors and countries. In this study, sovereign credit rating methodologies of CRAs and studies in relevant literature are examined in detail, and two dynamic methods are proposed. These models classify countries as investable or speculative in the short term. In the first model, we used stock market values and macroeconomic variables with the Normalized Least Mean Square (NLMS) algorithm. Ratings for 15 countries are determined according to the short-term domestic currency. The results that we obtained from this model are fully consistent with those of Fitch. When we compared the results with Standard and Poorâ€™s, we obtained different results for Turkey and Portugal. In the second model, we used only stock market closing data from 40 composite indexes with the Artificial Neural Networks (ANNs). Ratings are determined according to short-term foreign currency. The results that we acquired from these two models are fully compliant with Standard and Poor's. However, when compared to the ratings of Fitch, the results differed in the case of Russia. It has been shown that contrary to standard approaches, high predictability is achievable for countries using short-term data. The suggested models are more objective and dynamic due to only short-term data being required.Â
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