Ralf Stecking

Personal Details

First Name:	Ralf
Middle Name:	Werner
Last Name:	Stecking
Suffix:
RePEc Short-ID:	pst509
	[This author has chosen not to make the email address public]

Affiliation

Institut für Volkswirtschaftslehre
Carl von Ossietzky Universität Oldenburg

Oldenburg, Germany
http://www.vwl.uni-oldenburg.de/
RePEc:edi:fwoldde (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

1. K B Schebesch & R Stecking, 2005. "Support vector machines for classifying and describing credit applicants: detecting typical and critical regions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1082-1088, September.

   Cited by:

   1. Julio López & Sebastián Maldonado & Ricardo Montoya, 2017. "Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1323-1334, November.
   2. Doris Fejza & Dritan Nace & Orjada Kulla, 2022. "The Credit Risk Problem—A Developing Country Case Study," Risks, MDPI, vol. 10(8), pages 1-11, July.
   3. Yang, Yingxu, 2007. "Adaptive credit scoring with kernel learning methods," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1521-1536, December.
   4. Lili Li & Jun Yang & Xin Zou, 2016. "A study of credit risk of Chinese listed companies: ZPP versus KMV," Applied Economics, Taylor & Francis Journals, vol. 48(29), pages 2697-2710, June.
   5. Swati Anand & Kushendra Mishra, 2022. "Identifying potential millennial customers for financial institutions using SVM," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(4), pages 335-345, December.
   6. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
   7. Li, Jialiang & Zhang, Wenyang & Kong, Efang, 2018. "Factor models for asset returns based on transformed factors," Journal of Econometrics, Elsevier, vol. 207(2), pages 432-448.
   8. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
   9. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
   10. Nehrebecka Natalia, 2018. "Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(2), pages 54-73, June.
   11. Ahmed Almustfa Hussin Adam Khatir & Marco Bee, 2022. "Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?," Risks, MDPI, vol. 10(9), pages 1-22, August.
   12. Tang, Lingxiao & Cai, Fei & Ouyang, Yao, 2019. "Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 563-572.

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Corrections

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