Using DEA and financial ratings for credit risk evaluation: an empirical analysis

Iazzolino, Gianpaolo; Bruni, Maria Elena; Beraldi, Patrizia

doi:10.1080/13504851.2013.806771

Cited by 21 publications

(15 citation statements)

References 13 publications

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“…The most common techniques are machine learning algorithms, such as neural networks, kernel methods, classification trees and decision rules, fuzzy and neuro-fuzzy systems, Bayesian models, etc. (Bellotti & Crook, 2009b (He, Zhang, Shi, & Huang, 2010;Iazzolino, Bruni, & Beraldi, 2013;Li, Shi, & He, 2008;Peng, Kou, Shi, & Chen, 2008). Ensembles, combining multiple base models developed either through a single classifier or multiple algorithms to derive improved combined forecasts.…”

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

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“…These models are designed to classify firms as creditworthy or insolvent, allowing them to be identified as likely to default on their debt (Tsolas 2015). The development of a methodological framework for analyzing firm credit risk and its components has become critical in light of the recent economic crisis (Iazzolino et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach

Tsolas

2021

JRFM

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This paper employs a cross-sectional research design to collect quantitative data for a group of Greek pharmaceutical companies in order to evaluate their credit risk. The data are processed using a variety of quantitative approaches, including series two-stage data envelopment analysis (DEA) combined with bootstrap and hierarchical clustering. The results of the two-stage DEA bootstrapped analysis indicate that the key problem with the firms’ performance is a lack of effectiveness rather than operating efficiency. The lack of a correlation between operating efficiency and effectiveness indicates that the firms’ performance metrics are unrelated. As a result, a bootstrapped DEA-based synthetic indicator is developed to be used with the other performance metrics as inputs to hierarchical clustering to divide sample firms into credit risk clusters. The series two-stage DEA bootstrapped approach used in this study could aid firms in evaluating their performance and increasing their competitive advantages.

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“…(Pulic, 2000). Other studies were carried out on firm performance and different ways for analyzing it (Iazzolino et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Relational Capital and financial performance: an empirical analysis on a sample of Italian firms

Iazzolino

Chiappetta

Chiappetta³

2018

Problems and Perspectives in Management

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The aim of this paper is to study the relations existing between the relational capital, both internal and external, and the firm financial performance. The investigation has been carried out on quantitative and qualitative data extracted from a sample of 100 Italian large firms. The paper deeply analyzes the dimension of the internal relational capital, not so widely studied in the literature. Findings demonstrate the influence of the internal relational capital (IRC) and of the external relational capital (ERC) on performance. The research suggests that an effort has to be devoted not only to improving relations with external stakeholders, but also to developing intra-firm relations. The research contributes to studies in management and in particular in internal organization by demonstrating that designing and implementing systems for supporting internal relation can improve firm performance.

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Using DEA and financial ratings for credit risk evaluation: an empirical analysis

Cited by 21 publications

References 13 publications

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

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

Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach

Relational Capital and financial performance: an empirical analysis on a sample of Italian firms

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