IDEAS home Printed from https://ideas.repec.org/a/spr/rvmgts/v18y2024i7d10.1007_s11846-023-00660-x.html
   My bibliography  Save this article

Mean–variance vs trend–risk portfolio selection

Author

Listed:
  • David Neděla

    (VSB–Technical University of Ostrava)

  • Sergio Ortobelli

    (VSB–Technical University of Ostrava
    University of Bergamo)

  • Tomáš Tichý

    (VSB–Technical University of Ostrava)

Abstract

In this paper, we provide an alternative trend (time)-dependent risk measure to Ruttiens’ accrued returns variability (Ruttiens in Comput Econ 41:407–424, 2013). We propose to adjust the calculation procedure to achieve an alternative risk measure. Our modification eliminates static mean component and it is based on the deviation of squared dispersions, which reflects the trend (time factor) precisely. Moreover, we also present a new perspective on dependency measures and we apply a PCA to a new correlation matrix in order to determine a parametric and nonparametric return approximation. In addition, the two-phase portfolio selection strategy is considered, where the mean–variance portfolio selection strategies represent the first optimization. The second one is the minimization of deviations from their trend leading to identical mean and final wealth. Finally, an empirical analysis verify the property and benefit of portfolio selection strategies based on these trend-dependent measures. In particular, the ex-post results show that applying the modified measure allows us to reduce the risk with respect to the trend of several portfolio strategies.

Suggested Citation

  • David Neděla & Sergio Ortobelli & Tomáš Tichý, 2024. "Mean–variance vs trend–risk portfolio selection," Review of Managerial Science, Springer, vol. 18(7), pages 2047-2078, July.
    • Handle: RePEc:spr:rvmgts:v:18:y:2024:i:7:d:10.1007_s11846-023-00660-x
    DOI: 10.1007/s11846-023-00660-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11846-023-00660-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11846-023-00660-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sergio Ortobelli & Tomáš Tichý, 2015. "On the impact of semidefinite positive correlation measures in portfolio theory," Annals of Operations Research, Springer, vol. 235(1), pages 625-652, December.
    2. Edward I. Altman, 1998. "Credit Risk Measurement and Management: The Ironic Challenge in the Next Decade," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-003, New York University, Leonard N. Stern School of Business-.
    3. B. Fastrich & S. Paterlini & P. Winker, 2015. "Constructing optimal sparse portfolios using regularization methods," Computational Management Science, Springer, vol. 12(3), pages 417-434, July.
    4. Spronk, J. & Hallerbach, W.G.P.M., 2002. "The Relevance of MCDM for Financial Decisions," ERIM Report Series Research in Management ERS-2002-69-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Egozcue, Martín & García, Luis Fuentes & Wong, Wing-Keung & Zitikis, Ricardas, 2011. "Do investors like to diversify? A study of Markowitz preferences," European Journal of Operational Research, Elsevier, vol. 215(1), pages 188-193, November.
    6. Nolan, John P. & Ojeda-Revah, Diana, 2013. "Linear and nonlinear regression with stable errors," Journal of Econometrics, Elsevier, vol. 172(2), pages 186-194.
    7. Sergio Ortobelli & Noureddine Kouaissah & Tomáš Tichý, 2019. "On the use of conditional expectation in portfolio selection problems," Annals of Operations Research, Springer, vol. 274(1), pages 501-530, March.
    8. Svetlozar Rachev & Sergio Ortobelli & Stoyan Stoyanov & Frank J. Fabozzi & Almira Biglova, 2008. "Desirable Properties Of An Ideal Risk Measure In Portfolio Theory," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 19-54.
    9. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    10. Almira Biglova & Sergio Ortobelli & Frank J Fabozzi, 2014. "Portfolio selection in the presence of systemic risk," Journal of Asset Management, Palgrave Macmillan, vol. 15(5), pages 285-299, October.
    11. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    12. Moorman, Theodore, 2014. "An empirical investigation of methods to reduce transaction costs," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 230-246.
    13. Fan, Jianqing & Fan, Yingying & Lv, Jinchi, 2008. "High dimensional covariance matrix estimation using a factor model," Journal of Econometrics, Elsevier, vol. 147(1), pages 186-197, November.
    14. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noureddine Kouaissah & Sergio Ortobelli Lozza & Ikram Jebabli, 2022. "Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 833-859, October.
    2. Kouaissah, Noureddine, 2021. "Using multivariate stochastic dominance to enhance portfolio selection and warn of financial crises," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 480-493.
    3. Sergio Ortobelli & Noureddine Kouaissah & Tomáš Tichý, 2017. "On the impact of conditional expectation estimators in portfolio theory," Computational Management Science, Springer, vol. 14(4), pages 535-557, October.
    4. Sergio Ortobelli & Noureddine Kouaissah & Tomáš Tichý, 2019. "On the use of conditional expectation in portfolio selection problems," Annals of Operations Research, Springer, vol. 274(1), pages 501-530, March.
    5. Noureddine Kouaissah & Amin Hocine, 2021. "Forecasting systemic risk in portfolio selection: The role of technical trading rules," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 708-729, July.
    6. Malavasi, Matteo & Ortobelli Lozza, Sergio & Trück, Stefan, 2021. "Second order of stochastic dominance efficiency vs mean variance efficiency," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1192-1206.
    7. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    8. Rachev, Svetlozar & Jasic, Teo & Stoyanov, Stoyan & Fabozzi, Frank J., 2007. "Momentum strategies based on reward-risk stock selection criteria," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2325-2346, August.
    9. Adabi Firouzjaee , Bagher & Mehrara , Mohsen & Mohammadi , Shapour, 2014. "Optimal Portfolio Selection for Tehran Stock Exchange Using Conditional, Partitioned and Worst-case Value at Risk Measures," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 9(1), pages 1-30, October.
    10. Marco Corazza & Giovanni Fasano & Riccardo Gusso, 2011. "Particle Swarm Optimization with non-smooth penalty reformulation for a complex portfolio selection problem," Working Papers 2011_10, Department of Economics, University of Venice "Ca' Foscari".
    11. Taras Bodnar & Mathias Lindholm & Erik Thorsén & Joanna Tyrcha, 2021. "Quantile-based optimal portfolio selection," Computational Management Science, Springer, vol. 18(3), pages 299-324, July.
    12. Martin Branda & Max Bucher & Michal Červinka & Alexandra Schwartz, 2018. "Convergence of a Scholtes-type regularization method for cardinality-constrained optimization problems with an application in sparse robust portfolio optimization," Computational Optimization and Applications, Springer, vol. 70(2), pages 503-530, June.
    13. 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.
    14. Davide Lauria & W. Brent Lindquist & Svetlozar T. Rachev, 2023. "Enhancing CVaR portfolio optimisation performance with GAM factor models," Papers 2401.00188, arXiv.org.
    15. Sergio Ortobelli & Sebastiano Vitali & Marco Cassader & Tomáš Tichý, 2018. "Portfolio selection strategy for fixed income markets with immunization on average," Annals of Operations Research, Springer, vol. 260(1), pages 395-415, January.
    16. Amita Sharma & Sebastian Utz & Aparna Mehra, 2017. "Omega-CVaR portfolio optimization and its worst case analysis," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 505-539, March.
    17. Goh, Joel Weiqiang & Lim, Kian Guan & Sim, Melvyn & Zhang, Weina, 2012. "Portfolio value-at-risk optimization for asymmetrically distributed asset returns," European Journal of Operational Research, Elsevier, vol. 221(2), pages 397-406.
    18. Dias, Alexandra, 2016. "The economic value of controlling for large losses in portfolio selection," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 81-91.
    19. Kouaissah, Noureddine, 2023. "Robust reward-risk performance measures with weakly second-order stochastic dominance constraints," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 53-62.
    20. Georgios Mamanis, 2021. "Analyzing the Performance of a Two-Tail-Measures-Utility Multi-objective Portfolio Optimization Model," SN Operations Research Forum, Springer, vol. 2(4), pages 1-18, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:rvmgts:v:18:y:2024:i:7:d:10.1007_s11846-023-00660-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.