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Nonnegative group bridge and application in financial index tracking

Author

Listed:
  • Yonghui Liu

    (Shanghai University of International Business and Economics)

  • Yichen Lin

    (Shanghai University of International Business and Economics)

  • Xin Song

    (Shanghai University of International Business and Economics)

  • Conan Liu

    (University of New South Wales)

  • Shuangzhe Liu

    (University of Canberra)

Abstract

The stock index plays an increasingly important role in investors’ decision-making. With the continuous development of the stock markets and the advancement of financial technology, the methods of compiling stock indices have consistently improved. Index tracking attempts to match the performance of a target market index by setting up a portfolio of assets to obtain similar returns to the target index. Therefore, the methods of selecting which stocks constitute a portfolio are very important. In daily investing, investors select quality assets from the target index to include in their tracking portfolio. In this paper, a nonnegative group bridge method is proposed for variable selection and estimation of grouping variables without overlapping to aid stock selection. The estimation consistency, variable-selection consistency, and asymptotic property of this method are provided. To obtain the solution of this model, we use an idea based on the local group coordinate descent method. Using tracking error as the criterion, the nonnegative group bridge estimation method is found superior to other nonnegative methods in terms of goodness-of-fit.

Suggested Citation

  • Yonghui Liu & Yichen Lin & Xin Song & Conan Liu & Shuangzhe Liu, 2024. "Nonnegative group bridge and application in financial index tracking," Statistical Papers, Springer, vol. 65(2), pages 887-907, April.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:2:d:10.1007_s00362-023-01406-3
    DOI: 10.1007/s00362-023-01406-3
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    References listed on IDEAS

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    1. Jian Huang & Shuange Ma & Huiliang Xie & Cun-Hui Zhang, 2009. "A group bridge approach for variable selection," Biometrika, Biometrika Trust, vol. 96(2), pages 339-355.
    2. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    3. Chen, Qi-an & Hu, Qingyu & Yang, Hu & Qi, Kai, 2022. "A kind of new time-weighted nonnegative lasso index-tracking model and its application," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    4. Wu, Lan & Yang, Yuehan & Liu, Hanzhong, 2014. "Nonnegative-lasso and application in index tracking," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 116-126.
    5. Yalian Li & Hu Yang, 2012. "A new Liu-type estimator in linear regression model," Statistical Papers, Springer, vol. 53(2), pages 427-437, May.
    6. Ning Li & Hu Yang, 2021. "Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models," Statistical Papers, Springer, vol. 62(2), pages 661-680, April.
    7. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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