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Best-Case Scenario Robust Portfolio: Evidence from China Stock Market

Author

Listed:
  • Kaiqiang An

    (China West Normal University)

  • Guiyu Zhao

    (China West Normal University)

  • Jinjun Li

    (Sichuan Tourism University)

  • Jingsong Tian

    (China West Normal University)

  • Lihua Wang

    (China West Normal University)

  • Liang Xian

    (Chengdu Sport University)

  • Chen Chen

    (Sichuan Tourism University)

Abstract

Stock markets are flooded with various uncertainties such as stock market scenarios, especially the input parameters of stock portfolio models. It is hard for stock market investments to jointly deal with them. Although robust portfolio is the prevailing portfolio policy handling with uncertainties, it always focuses on the worst-case scenario of all the possible realizations of uncertain input parameters, resulting in the optimal portfolios considerably conservative as well as the portfolio performances very inferior. To this end, based on Markowitz’s mean–variance (MV) model, this paper builds the new robust portfolio model (RMV-best) from the innovative perspective of best-case scenario, exactly contrary to the model based on the worst-case scenario (RMV-worst). Furthermore, stock market scenarios which always shift uncertainly show the periodic characteristics, and can be divided into three movement statuses: the bull, the bear and the steady. To verify the consistency of RMV-best, RMV-worst and MV portfolio policies over the different motion periods and identify the differences of these portfolio policies at various movement statuses, the mix data including two motion periods and three industry sectors is employed. Eventually, empirical results indicate that RMV-best is always the most favorable portfolio policy at the bull and the bear market, while MV can produce more profitable portfolios at the steady market over two motion periods. However, the drawbacks of RMV-worst are also confirmed in this study.

Suggested Citation

  • Kaiqiang An & Guiyu Zhao & Jinjun Li & Jingsong Tian & Lihua Wang & Liang Xian & Chen Chen, 2023. "Best-Case Scenario Robust Portfolio: Evidence from China Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(2), pages 297-322, June.
  • Handle: RePEc:kap:apfinm:v:30:y:2023:i:2:d:10.1007_s10690-022-09375-7
    DOI: 10.1007/s10690-022-09375-7
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    References listed on IDEAS

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    1. Ehrgott, Matthias & Ide, Jonas & Schöbel, Anita, 2014. "Minmax robustness for multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 239(1), pages 17-31.
    2. Upton, Gregory B. & Snyder, Brian F., 2017. "Funding renewable energy: An analysis of renewable portfolio standards," Energy Economics, Elsevier, vol. 66(C), pages 205-216.
    3. González-Pedraz, Carlos & Moreno, Manuel & Peña, Juan Ignacio, 2014. "Tail risk in energy portfolios," Energy Economics, Elsevier, vol. 46(C), pages 422-434.
    4. Scala, Antonio & Facchini, Angelo & Perna, Umberto & Basosi, Riccardo, 2019. "Portfolio analysis and geographical allocation of renewable sources: A stochastic approach," Energy Policy, Elsevier, vol. 125(C), pages 154-159.
    5. Lotfi, Somayyeh & Zenios, Stavros A., 2018. "Robust VaR and CVaR optimization under joint ambiguity in distributions, means, and covariances," European Journal of Operational Research, Elsevier, vol. 269(2), pages 556-576.
    6. Gatfaoui, Hayette, 2016. "Linking the gas and oil markets with the stock market: Investigating the U.S. relationship," Energy Economics, Elsevier, vol. 53(C), pages 5-16.
    7. Costa, Oswaldo L.V. & de Oliveira Ribeiro, Celma & Rego, Erik Eduardo & Stern, Julio Michael & Parente, Virginia & Kileber, Solange, 2017. "Robust portfolio optimization for electricity planning: An application based on the Brazilian electricity mix," Energy Economics, Elsevier, vol. 64(C), pages 158-169.
    8. Jang Ho Kim & Woo Chang Kim & Frank J. Fabozzi, 2014. "Recent Developments in Robust Portfolios with a Worst-Case Approach," Journal of Optimization Theory and Applications, Springer, vol. 161(1), pages 103-121, April.
    9. Jonas Ide & Elisabeth Köbis, 2014. "Concepts of efficiency for uncertain multi-objective optimization problems based on set order relations," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 80(1), pages 99-127, August.
    10. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    11. D. Goldfarb & G. Iyengar, 2003. "Robust Portfolio Selection Problems," Mathematics of Operations Research, INFORMS, vol. 28(1), pages 1-38, February.
    12. Frank Fabozzi & Dashan Huang & Guofu Zhou, 2010. "Robust portfolios: contributions from operations research and finance," Annals of Operations Research, Springer, vol. 176(1), pages 191-220, April.
    13. Xing, Xin & Hu, Jinjin & Yang, Yaning, 2014. "Robust minimum variance portfolio with L-infinity constraints," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 107-117.
    14. Jiang, Chonghui & Du, Jiangze & An, Yunbi, 2019. "Combining the minimum-variance and equally-weighted portfolios: Can portfolio performance be improved?," Economic Modelling, Elsevier, vol. 80(C), pages 260-274.
    15. Zhilin Kang & Zhongfei Li, 2018. "An exact solution to a robust portfolio choice problem with multiple risk measures under ambiguous distribution," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 87(2), pages 169-195, April.
    16. Xidonas, Panos & Hassapis, Christis & Soulis, John & Samitas, Aristeidis, 2017. "Robust minimum variance portfolio optimization modelling under scenario uncertainty," Economic Modelling, Elsevier, vol. 64(C), pages 60-71.
    17. Kolm, Petter N. & Tütüncü, Reha & Fabozzi, Frank J., 2014. "60 Years of portfolio optimization: Practical challenges and current trends," European Journal of Operational Research, Elsevier, vol. 234(2), pages 356-371.
    18. Kim, Woo Chang & Kim, Jang Ho & Mulvey, John M. & Fabozzi, Frank J., 2015. "Focusing on the worst state for robust investing," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 19-31.
    19. Bernd Scherer, 2007. "Can robust portfolio optimisation help to build better portfolios?," Journal of Asset Management, Palgrave Macmillan, vol. 7(6), pages 374-387, March.
    20. Li, Ping & Han, Yingwei & Xia, Yong, 2016. "Portfolio optimization using asymmetry robust mean absolute deviation model," Finance Research Letters, Elsevier, vol. 18(C), pages 353-362.
    21. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
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