Automated Credit Rating Prediction in a competitive framework

Abstract

Automated credit rating prediction (ACRP) algorithms are used to predict the ratings of bonds without having to trust one rating agency, like Moody's, Fitch or S&P. Nevertheless, for the moment, the accuracy of ACRP algorithms is investigated by empirical tests. In this paper, the framework for a competitive analysis is set and afterwards in this framework, the definition of competitive ACRP algorithms and its demonstration is given. In this way, for a competitive ACRP algorithm, a worst-case guarantee concerning the misclassification error is offered. Furthermore, several ACRP algorithms from the literature are compared according their competitiveness.

A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees

“…The authors used multiple datasets and criteria for evaluation and concluded that their hybrid method is superior to other methods. Gangolf et al Gangolf et al (2016) provided a comparison of statistical and artificial intelligence techniques used for forecasting probability of default for bonds. In case of predicting probability of default, Angelini et al (Angelini et al, 2008) studied two different neural networks to evaluate credit risk of Italian small businesses.…”

Optimal cash management with uncertain, interrelated and bounded demands

“…[20] established this ratio which is widely used for other online financial problems, e.g. online portfolio selection [5] and automated credit rating prediction [8]. In simple terms, the competitive ratio of an online algorithm problem is the maximum possible quotient in terms of costs incurred by the online algorithm and the best possible solution.…”

Optimal online algorithms for the portfolio selection problem, bi-directional trading and -search with interrelated prices

“…Solutions that incur the lowest ratio are called optimal. Recently, Gangolf et al [8] applied the concept of competitive ratio to automated credit rating prediction algorithms in order to evaluate their performance in worst-case scenarios. Schroeder and Kacem [15] derived optimal online algorithms for the cash-management problem with uncertain and interrelated demands.…”