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Credit Rating Forecasting Using Machine Learning Techniques

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Credit Rating Forecasting Using Machine Learning Techniques. / Wallis, Mark; Kumar, Kuldeep; Gepp, Adrian.
Research Anthology on Machine Learning Techniques, Methods, and Applications. United States: IGI Global, 2022. p. 734-752.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

HarvardHarvard

Wallis, M, Kumar, K & Gepp, A 2022, Credit Rating Forecasting Using Machine Learning Techniques. in Research Anthology on Machine Learning Techniques, Methods, and Applications. IGI Global, United States, pp. 734-752. https://doi.org/10.4018/978-1-6684-6291-1.ch039

APA

Wallis, M., Kumar, K., & Gepp, A. (2022). Credit Rating Forecasting Using Machine Learning Techniques. In Research Anthology on Machine Learning Techniques, Methods, and Applications (pp. 734-752). IGI Global. https://doi.org/10.4018/978-1-6684-6291-1.ch039

CBE

Wallis M, Kumar K, Gepp A. 2022. Credit Rating Forecasting Using Machine Learning Techniques. In Research Anthology on Machine Learning Techniques, Methods, and Applications. United States: IGI Global. pp. 734-752. https://doi.org/10.4018/978-1-6684-6291-1.ch039

MLA

Wallis, Mark, Kuldeep Kumar, and Adrian Gepp "Credit Rating Forecasting Using Machine Learning Techniques". Research Anthology on Machine Learning Techniques, Methods, and Applications. United States: IGI Global. 2022, 734-752. https://doi.org/10.4018/978-1-6684-6291-1.ch039

VancouverVancouver

Wallis M, Kumar K, Gepp A. Credit Rating Forecasting Using Machine Learning Techniques. In Research Anthology on Machine Learning Techniques, Methods, and Applications. United States: IGI Global. 2022. p. 734-752 doi: 10.4018/978-1-6684-6291-1.ch039

Author

Wallis, Mark ; Kumar, Kuldeep ; Gepp, Adrian. / Credit Rating Forecasting Using Machine Learning Techniques. Research Anthology on Machine Learning Techniques, Methods, and Applications. United States : IGI Global, 2022. pp. 734-752

RIS

TY - CHAP

T1 - Credit Rating Forecasting Using Machine Learning Techniques

AU - Wallis, Mark

AU - Kumar, Kuldeep

AU - Gepp, Adrian

PY - 2022/5/1

Y1 - 2022/5/1

N2 - Credit ratings are an important metric for business managers and a contributor to economic growth. Forecasting such ratings might be a suitable application of big data analytics. As machine learning is one of the foundations of intelligent big data analytics, this chapter presents a comparative analysis of traditional statistical models and popular machine learning models for the prediction of Moody's long-term corporate debt ratings. Machine learning techniques such as artificial neural networks, support vector machines, and random forests generally outperformed their traditional counterparts in terms of both overall accuracy and the Kappa statistic. The parametric models may be hindered by missing variables and restrictive assumptions about the underlying distributions in the data. This chapter reveals the relative effectiveness of non-parametric big data analytics to model a complex process that frequently arises in business, specifically determining credit ratings.*********************************************************************************************Chapter reprinted. Originally published in Managerial Perspectives on Intelligent Big Data Analytics edited by Zhaohao Sun. IGI Global, 2019*********************************************************************************************

AB - Credit ratings are an important metric for business managers and a contributor to economic growth. Forecasting such ratings might be a suitable application of big data analytics. As machine learning is one of the foundations of intelligent big data analytics, this chapter presents a comparative analysis of traditional statistical models and popular machine learning models for the prediction of Moody's long-term corporate debt ratings. Machine learning techniques such as artificial neural networks, support vector machines, and random forests generally outperformed their traditional counterparts in terms of both overall accuracy and the Kappa statistic. The parametric models may be hindered by missing variables and restrictive assumptions about the underlying distributions in the data. This chapter reveals the relative effectiveness of non-parametric big data analytics to model a complex process that frequently arises in business, specifically determining credit ratings.*********************************************************************************************Chapter reprinted. Originally published in Managerial Perspectives on Intelligent Big Data Analytics edited by Zhaohao Sun. IGI Global, 2019*********************************************************************************************

U2 - 10.4018/978-1-6684-6291-1.ch039

DO - 10.4018/978-1-6684-6291-1.ch039

M3 - Chapter

SN - 9781668462911

SP - 734

EP - 752

BT - Research Anthology on Machine Learning Techniques, Methods, and Applications

PB - IGI Global

CY - United States

ER -