Comparison of support vector machine and support vector regression - An application to predict financial distress and bankruptcy

Mu Yen Chen; Chia Chen Chen; Ya Fen Chang

doi:10.1109/ICSSSM.2010.5530111

Comparison of support vector machine and support vector regression - An application to predict financial distress and bankruptcy

Mu Yen Chen, Chia Chen Chen, Ya Fen Chang

Department of Engineering Science

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

4 Citations (Scopus)

Abstract

Lately, many notorious financial distress and bankruptcy events occurred in the world economic. As we known, bankruptcy of Lehman Brothers Holdings Inc. (LEH) is the largest bankruptcy filing in U.S. history in 2008. These events have serious impacted on the socio-economic and investment in public wealth. Due to solve this dilemma, this research collected 68 listed companies as the raw data from Taiwan Stock Exchange Corporation (TSEC). The support vector machine (SVM) and support vector regression (SVR) techniques were used to implement the financial distress prediction model. Moreover, we adopted a total of 22 ratios which composed of 13 financial ratios and 9 macroeconomic indexes to be the input variables for these models. Finally, the experiments obtained the accuracy rate, Type II error rate and RMSE (root mean squared error) of these classification methods for the financial distress and bankruptcy prediction.

Original language	English
Title of host publication	2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10
Pages	923-928
Number of pages	6
DOIs	https://doi.org/10.1109/ICSSSM.2010.5530111
Publication status	Published - 2010
Event	7th International Conference on Service Systems and Service Management, ICSSSM'10 - Tokyo, Japan Duration: 2010 Jun 28 → 2010 Jun 30

Publication series

Name	2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10

Conference

Conference	7th International Conference on Service Systems and Service Management, ICSSSM'10
Country/Territory	Japan
City	Tokyo
Period	10-06-28 → 10-06-30

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Control and Systems Engineering

Access to Document

10.1109/ICSSSM.2010.5530111

Cite this

Chen, M. Y., Chen, C. C., & Chang, Y. F. (2010). Comparison of support vector machine and support vector regression - An application to predict financial distress and bankruptcy. In 2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10 (pp. 923-928). Article 5530111 (2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10). https://doi.org/10.1109/ICSSSM.2010.5530111

Chen, Mu Yen ; Chen, Chia Chen ; Chang, Ya Fen. / Comparison of support vector machine and support vector regression - An application to predict financial distress and bankruptcy. 2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10. 2010. pp. 923-928 (2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10).

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abstract = "Lately, many notorious financial distress and bankruptcy events occurred in the world economic. As we known, bankruptcy of Lehman Brothers Holdings Inc. (LEH) is the largest bankruptcy filing in U.S. history in 2008. These events have serious impacted on the socio-economic and investment in public wealth. Due to solve this dilemma, this research collected 68 listed companies as the raw data from Taiwan Stock Exchange Corporation (TSEC). The support vector machine (SVM) and support vector regression (SVR) techniques were used to implement the financial distress prediction model. Moreover, we adopted a total of 22 ratios which composed of 13 financial ratios and 9 macroeconomic indexes to be the input variables for these models. Finally, the experiments obtained the accuracy rate, Type II error rate and RMSE (root mean squared error) of these classification methods for the financial distress and bankruptcy prediction.",

author = "Chen, {Mu Yen} and Chen, {Chia Chen} and Chang, {Ya Fen}",

note = "Copyright: Copyright 2010 Elsevier B.V., All rights reserved.; 7th International Conference on Service Systems and Service Management, ICSSSM'10 ; Conference date: 28-06-2010 Through 30-06-2010",

year = "2010",

doi = "10.1109/ICSSSM.2010.5530111",

language = "English",

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booktitle = "2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10",

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Chen, MY, Chen, CC & Chang, YF 2010, Comparison of support vector machine and support vector regression - An application to predict financial distress and bankruptcy. in 2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10., 5530111, 2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10, pp. 923-928, 7th International Conference on Service Systems and Service Management, ICSSSM'10, Tokyo, Japan, 10-06-28. https://doi.org/10.1109/ICSSSM.2010.5530111

Comparison of support vector machine and support vector regression - An application to predict financial distress and bankruptcy. / Chen, Mu Yen; Chen, Chia Chen; Chang, Ya Fen.
2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10. 2010. p. 923-928 5530111 (2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10).

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

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Chen MY, Chen CC, Chang YF. Comparison of support vector machine and support vector regression - An application to predict financial distress and bankruptcy. In 2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10. 2010. p. 923-928. 5530111. (2010 7th International Conference on Service Systems and Service Management, Proceedings of ICSSSM' 10). doi: 10.1109/ICSSSM.2010.5530111

Comparison of support vector machine and support vector regression - An application to predict financial distress and bankruptcy

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