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Index Tracking by Using Sparse Support Vector Regression

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Intelligence Science and Big Data Engineering (IScIDE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

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Abstract

In this paper a sparse support vector regression (SVR) model and its solution method are considered for the index tracking problem. The sparse SVR model is structured by adding a cardinality constraint in a \(\varepsilon \)-SVR model and the piecewise linear functions are used to simplify the model. In addition, for simplifying the parameter selection of the model a sparse variation of the \(\nu \)-SVR model is considered too. The two models are solved by utilizing the penalty proximal alternating linearized minimization (PALM) method and the structures of the two models satisfy the convergence conditions of the penalty PALM method. The numerical results with practical data sets demonstrate that for the fewer sample data the sparse SVR models have better generalization ability and stability especially for the large-scale problems.

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Acknowledgments

The authors are grateful to anonymous reviewers for many helpful suggestions.

Funding

This work was supported by the National Natural Science Foundation of China (11571061, 11301050) and the Fundamental Research Funds for the Central Universities (DUT15RC(3)037).

Author information

Authors and Affiliations

  1. School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Yue Teng & Bo Yu

  2. School of Science, Dalian University of Technology, Panjin, 124221, People’s Republic of China

    Li Yang & Kunpeng Yuan

Authors
  1. Yue Teng
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  2. Li Yang
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  3. Kunpeng Yuan
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  4. Bo Yu
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Corresponding author

Correspondence to Bo Yu .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

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Teng, Y., Yang, L., Yuan, K., Yu, B. (2017). Index Tracking by Using Sparse Support Vector Regression. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_26

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  • DOI: https://doi.org/10.1007/978-3-319-67777-4_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

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