Description
Title: Statistical Forecasting for Industrial and Retail Firms by Moody’s
Abstract: Long-term company ratings are derived from publicly available data and some additional, non-disclosed information. To obtain these ratings directly, a model based on information from firms’ public accounts is suggested, and it exhibits a degree of similarity to published findings from credit rating agencies. Given that companies fund the majority of the rating process and are thus clients of the rating firms, there may be some potential conflicts of interest in the rating models used to evaluate a firm’s creditworthiness. During the 2008 financial crisis, the lack of trust among investors and the criticism of the rating agencies was particularly severe. Several solutions are discussed to address this problem; in particular, the emphasis is on developing a rating model for Moody’s long-term companies’ ratings for industrial and retailing firms that could be helpful as an external check of published rates. Without aggregating adjacent classes, as is typical in earlier literature, statistical and artificial intelligence methods are used to obtain direct predictions of awarded rates in these sectors. This method produces real rating forecasts that are simple to replicate and rely only on publicly accessible data, all while being more accurate and avoiding the costs associated with the rating process. These models can be applied to other industries with more sampling data.
Keywords: Moody’s rating; forecasting; industrial firms; retailing; credit risk; neural networks
Paper Quality: SCOPUS / Web of Science Level Research Paper
Subject: Economics
Writer Experience: 20+ Years
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