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 databases, utilizing 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 high level of accuracy. This also permits to check the published rating’s reliability provided by different CRAs.

Bond rating determinants and modeling: evidence from India

“…West (2000) and Baesens et al (2003) investigate the performance of the ANN model in credit rating and show that the ANN model performs better than the traditional statistical methods. Several studies (Garavaglia, 1991;Moody and Utans, 1994;Huang et al, 2004;Kim, 2005;Brabazon and O'Neill, 2006;Cao et al, 2006;Lee, 2007;Yu et al, 2008;Khashman, 2010;Kim and Sohn, 2010;Hajek, 2012;Hajek and Olej, 2014;Khemakhem and Boujelbene, 2015;Zhao et al, 2015;Addo et al, 2018;Caridad et al, 2019;Wallis et al, 2019) have analyzed the efficacy of ANN in credit rating models. ANN models the way information flows and decisionmaking happens in the nervous system.…”

Better results of artificial neural networks in predicting ČEZ share prices

“…For example, Caridad et al (2019) prove that artificial neural networks produce more disaggregated results when constructing ratings than some multivariate statistical methods and are used to assess ratings of credit rating agencies. Gamaliy et al (2019) consider the possibility of using artificial neural networks to analyze the impact of political and economic factors on the situation in the global foreign-exchange market.…”