Corporate rating forecasting using Artificial Intelligence statistical techniques

Abstract

Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&P, Moody’s, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called ‘qualitative information’. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial data bases, employing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neural Networks (ANN) provide better results than Multivariate statistical methods, and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies ‘public data' obtained from Bloomberg are used to obtain forecasts, of S&P and Moody’s ratings, directly from these data, with a high level of accuracy. This also permits to check the published rating's reliability provided by different CRAs.