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Robust equity portfolio performance

Author

Listed:
  • Jang Ho Kim

    (Kyung Hee University)

  • Woo Chang Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Do-Gyun Kwon

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Frank J. Fabozzi

    (EDHEC Business School)

Abstract

The earliest documented analytical approach to portfolio selection is Markowitz’s mean–variance analysis, which attempts to find the portfolio with optimal performance by considering the tradeoff between return and risk. The performance of mean–variance analysis has been the subject of many studies and compared to other portfolio construction approaches such as a naïve equally-weighted allocation scheme. In recent years, several approaches have been proposed to improve the mean–variance model by reducing the sensitivity of the portfolio selection process in order achieve robust performance. Although robust portfolio optimization has been one of the most researched methods for improving portfolio robustness, the performance of robust portfolios has not been the major focus of studies. In this paper, a comprehensive analysis on robust portfolio performance is presented for equity portfolios constructed in the U.S. market during the period 1980 and 2014, and results confirm the advantage of robust portfolio optimization for controlling uncertainty while efficiently allocating investments.

Suggested Citation

  • Jang Ho Kim & Woo Chang Kim & Do-Gyun Kwon & Frank J. Fabozzi, 2018. "Robust equity portfolio performance," Annals of Operations Research, Springer, vol. 266(1), pages 293-312, July.
    • Handle: RePEc:spr:annopr:v:266:y:2018:i:1:d:10.1007_s10479-017-2739-1
    DOI: 10.1007/s10479-017-2739-1
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    References listed on IDEAS

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    Cited by:

    1. Junna Bi & Jun Cai & Yan Zeng, 2021. "Equilibrium reinsurance-investment strategies with partial information and common shock dependence," Annals of Operations Research, Springer, vol. 307(1), pages 1-24, December.
    2. Jang Ho Kim & Yongjae Lee & Woo Chang Kim & Frank J. Fabozzi, 2022. "Goal-based investing based on multi-stage robust portfolio optimization," Annals of Operations Research, Springer, vol. 313(2), pages 1141-1158, June.
    3. Antonios Georgantas & Michalis Doumpos & Constantin Zopounidis, 2024. "Robust optimization approaches for portfolio selection: a comparative analysis," Annals of Operations Research, Springer, vol. 339(3), pages 1205-1221, August.
    4. Ni, Xuanming & Zheng, Tiantian & Zhao, Huimin & Zhu, Shushang, 2023. "High-dimensional portfolio optimization based on tree-structured factor model," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
    5. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2022. "Robust portfolio selection problems: a comprehensive review," Operational Research, Springer, vol. 22(4), pages 3203-3264, September.
    6. Giuseppe Pandolfo & Carmela Iorio & Roberta Siciliano & Antonio D’Ambrosio, 2020. "Robust mean-variance portfolio through the weighted $$L^{p}$$ L p depth function," Annals of Operations Research, Springer, vol. 292(1), pages 519-531, September.
    7. Jingnan Chen & Gengling Dai & Ning Zhang, 2020. "An application of sparse-group lasso regularization to equity portfolio optimization and sector selection," Annals of Operations Research, Springer, vol. 284(1), pages 243-262, January.
    8. Anja Vinzelberg & Benjamin R. Auer, 2022. "Unprofitability of food market investments," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(7), pages 2887-2910, October.
    9. Damian Kisiel & Denise Gorse, 2021. "A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection," Papers 2111.05935, arXiv.org.
    10. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2021. "Robust Portfolio Selection Problems: A Comprehensive Review," Papers 2103.13806, arXiv.org, revised Jan 2022.

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