Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms

Doumpos, Michael; Niklis, Dimitrios; Zopounidis, Constantin; Andriosopoulos, Kostas

doi:10.1016/j.jbankfin.2014.01.010

Cited by 50 publications

(37 citation statements)

References 29 publications

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“…Popular ensemble approaches include several variants of bagging and boosting algorithms, which have been shown to provide very good results in several cases (Abell an & Castellano, 2017; Bequ e & Lessmann, 2017; Finlay, 2011; Marqu es, Garc ıa, & S anchez, 2012). Hybrid systems, which rely on the combination of different techniques for feature/sample selection and model fitting as well as different modeling schemes (Doumpos, Niklis, Zopounidis, & Andriosopoulos, 2015;Niklis, Doumpos, & Zopounidis, 2014;Oreski, Oreski, & Oreski, 2012;Yeh, Lin, & Hsu, 2012;Yu, Wang, & Lai, 2009;Zhang, Gao, & Shi, 2014).…”

Section: Study Methodology Assetmentioning

confidence: 99%

Computational approaches and data analytics in financial services: A literature review

Andriosopoulos

Doumpos

Pardalos

et al. 2019

Journal of the Operational Research Society

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The level of modeling sophistication in financial services has increased considerably over the years. Nowadays, the complexity of financial problems and the vast amount of data require an engineering approach based on analytical modeling tools for planning, decision making, reporting, and supervisory control. This article provides an overview of the main financial applications of computational and data analytics approaches, focusing on the coverage of the recent developments and trends. The overview covers different methodological tools and their uses in areas, such as portfolio management, credit analysis, banking, and insurance.

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Section: Study Methodology Assetmentioning

confidence: 99%

Computational approaches and data analytics in financial services: A literature review

Andriosopoulos

Doumpos

Pardalos

et al. 2019

Journal of the Operational Research Society

Self Cite

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“…With that in our mind, we also turn to rating agencies and in particular to Fitch, to find out what matters when assigning a market implied rating. In other words, the selection of our explanatory variables is guided both from the existing empirical literature (see for example Kaplan and Urwitz, 1979;Ederington, 1985;Poon, 2003;Chava and Jarrow, 2004;Amendola et al, 2011;Mizen and Tsoukas, 2012;Hwang, 2013;Creal et al, 2014;Doumpos et al, 2015 andTian et al, 2015), and the common practice of rating agencies (see Fitch 2007 andLiu et al 2007). 7…”

Section: Choice Of Explanatory Variablesmentioning

confidence: 99%

Modelling market implied ratings using LASSO variable selection techniques

Sermpinis

Tsoukas

Zhang

2018

Journal of Empirical Finance

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“…Prior studies have also focused on U.S. companies due to better data availability, although several country-specific studies have examined Taiwanese [43], Korean [6], and European companies [2].…”

Section: Rating Grades and Datasetsmentioning

confidence: 99%

“…Since this evaluation can be slow and costly, automatic credit rating prediction has become a central problem in artificial intelligence (AI) research [1]. Prediction models have been extensively developed to replicate and explain the credit rating processes performed by credit rating agencies [2].…”

Section: Introductionmentioning

confidence: 99%

Predicting corporate investment/non-investment grade by using interval-valued fuzzy rule-based systems—A cross-region analysis

Hájek

2018

Applied Soft Computing

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Systems for predicting corporate rating have attracted considerable interest in soft computing research due to the requirements for both accuracy and interpretability. In addition, the high uncertainty associated primarily with linguistic uncertainties and disagreement among experts is another challenging problem. To overcome these problems, this study proposes a hybrid evolutionary interval-valued fuzzy rule-based system, namely IVTURS, combined with evolutionary feature selection component. This model is used to predict the investment/noninvestment grades of companies from four regions, namely Emerging countries, the EU, the United States, and other developed countries. To evaluate prediction performance, a yield measure is used that combines the return and default rates of companies. Here, we show that using interval-valued fuzzy sets leads to higher accuracy, particularly with the growing granularity at the fuzzy partition level. The proposed prediction model is then compared with several state-of-the-art evolutionary fuzzy rule-based systems. The obtained results show that the proposed model is especially suitable for high-dimensional problems, without facing rule base interpretability issues. This finding indicates that the model is preferable for investors oriented toward developed markets such as the EU and the United States.

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Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms

Cited by 50 publications

References 29 publications

Computational approaches and data analytics in financial services: A literature review

Computational approaches and data analytics in financial services: A literature review

Modelling market implied ratings using LASSO variable selection techniques

Predicting corporate investment/non-investment grade by using interval-valued fuzzy rule-based systems—A cross-region analysis

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