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

Content Provider	IEEE Xplore Digital Library
Author	Mu-Yen Chen Chia-Chen Chen Ya-Fen Chang
Copyright Year	2010
Description	Author affiliation: Department of Information Management, National Taichung Institute of Technology, 404 Taichung, Taiwan (Mu-Yen Chen) \|\| Department of Information Management, Tunghai University, 407 Taichung, Taiwan (Chia-Chen Chen) \|\| Department of Computer Science and Information Engineering, National Taichung Institute of Technology, 404 Taichung, Taiwan (Ya-Fen Chang)
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.
Starting Page	1
Ending Page	6
File Size	253265
Page Count	6
File Format	PDF
ISBN	9781424464852
e-ISBN	9781424464876
DOI	10.1109/ICSSSM.2010.5530111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-28
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Support Vector Regression Predictive models Financial Distress Support Vector Machine Biological neural networks Support vector machines Supervised learning Investments Support vector machine classification Classification Economic forecasting Macroeconomics Mathematical model
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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