An empirical evaluation of machine learning performance in corporate sales growth prediction-Reference-Cited by-同舟云学术

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An empirical evaluation of machine learning performance in corporate sales growth prediction

Published:2021 Issue:0 Volume:13 Page:25-28
ISSN:1883-0609
Container-title:JSIAM Letters
language:en
Short-container-title:JSIAM Letters

Author:

Saito Miho¹,Ohsato Takaya²,Yamanaka Suguru³⁴

Affiliation:

1. Graduate school of science and engineering, Aoyama Gakuin University

2. Center for data science education and resaerch, Shiga University

3. College of science and engineering, Aoyama Gakuin University

4. Institute of innovative research, Tokyo institute of technology

Publisher

The Japan Society for Industrial and Applied Mathematics

Link

https://www.jstage.jst.go.jp/article/jsiaml/13/0/13_25/_pdf

Reference23 articles.

1. 1) M. Ryser and S. Denzler, Selecting credit rating models: a cross-validation-based comparison of discriminatory power, Financial Mark. Portf. Manag., 23 (2009), 187-203.

2. 2) Z. Huang, H. Chen, C.-J. Hsu C.-J. Hsu, W.-H. Chen and S. Wu, Credit rating analysis with support vector machines and neural networks: a market comparative study, Decis. Support Syst., 37 (2004), 543-558.

3. 3) L. Cao, L. K. Guan and Z. Jingqing, Bond rating using support vector machine, Intell. Data Anal., 10 (2006), 285-296.

4. 4) Y.-C Lee, Application of support vector machines to corporate credit rating prediction, Expert Syst. Appl., 33 (2007), 67-74.

5. 5) K. Kim and H. Ahn, A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach, Comput. Oper. Res., 39 (2012), 1800-1811.

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1. Ellipsoidal buffered area under the curve maximization model with variable selection in credit risk estimation;Computational Management Science;2023-03-29

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