Abstract
In this paper, the performance of the optimal portfolio is studied when the portfolio optimization model is sensitive towards the expected rate of return of the assets. It is justified that any perturbation in the expected return within some bounds provide the decision maker with a set of choices of the optimal portfolio yielding portfolio risk within a range. Due to the perturbation in the parameters, the structure of the portfolio optimization models changes, and the classical approaches for solving these models are not suitable. Here, we represent these models with varying parameters as a system of interval equations and develop a methodology to obtain the set of all possible choices of the optimal portfolio of each model.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12597-024-00787-9/MediaObjects/12597_2024_787_Figa_HTML.png)
Similar content being viewed by others
Data availability
Data sharing not applicable to this article as no data sets were generated or analysed during the current study.
References
Mansini, R., Ogryczak, W., Speranza, M.G.: Twenty years of linear programming based portfolio optimization. Eur. J. Oper. Res. 234(2), 518–535 (2014)
Fang, Y., Lai, K., Wang, S.-Y.: Portfolio rebalancing model with transaction costs based on fuzzy decision theory. Eur. J. Oper. Res. 175(2), 879–893 (2006). https://doi.org/10.1016/j.ejor.2005.05.020. (https://www.sciencedirect.com/science/article/pii/S0377221705005102)
Best, M.J., Hlouskova, J.: An algorithm for portfolio optimization with transaction costs. Manage. Sci. 51(11), 1676–1688 (2005). (http://www.jstor.org/stable/20110455)
Fabozzi, F.J., Markowitz, H.M., Gupta, F.: Portfolio selection, Handbook of finance 2, (2008)
Konno, H., Koshizuka, T.: Mean-absolute deviation model. IIE Trans. 37(10), 893–900 (2005). https://doi.org/10.1080/07408170591007786
Meng, K., Yang, H., Yang, X., Wai Yu, C.K.: Portfolio optimization under a minimax rule revisited. Optimization 71(4), 877–905 (2022). https://doi.org/10.1080/02331934.2021.1928665
Lin, Y., Liu, S., Yang, H., Wu, H.: Stock trend prediction using candlestick charting and ensemble machine learning techniques with a novelty feature engineering scheme. IEEE Access 9, 101433–101446 (2021). https://doi.org/10.1109/ACCESS.2021.3096825
Mahmoodi, A., Hashemi, L., Jasemi, M.: Develop an integrated candlestick technical analysis model using meta-heuristic algorithms. EuroMed J. Bus. (2023). https://doi.org/10.1108/EMJB-02-2022-0034
Mahmoodi, A., Hashemi, L., Mahmoodi, A., Mahmoodi, B., Jasemi, M.: Novel comparative methodology of hybrid support vector machine with meta-heuristic algorithms to develop an integrated candlestick technical analysis model. J. Capital Markets Stud. (2023). https://doi.org/10.1108/JCMS-04-2023-0013
Mahmoodi, A., Hashemi, L., Jasemi, M., Laliberté, J., Millar, R.C., Noshadi, H.: A novel approach for candlestick technical analysis using a combination of the support vector machine and particle swarm optimization. Asian J. Econ. Bank. 7(1), 2–24 (2023). https://doi.org/10.1108/AJEB-11-2021-0131
Mahmoodi, A., Hashemi, L., Jasemi, M., Mehraban, S., Laliberté, J., Millar, R.C.: A developed stock price forecasting model using support vector machine combined with metaheuristic algorithms. Opsearch 60(1), 59–86 (2023). https://doi.org/10.1007/s12597-022-00608-x
Mahmoudi, A., Hashemi, L., Jasemi, M., Pope, J.: A comparison on particle swarm optimization and genetic algorithm performances in deriving the efficient frontier of stocks portfolios based on a ean-lower partial moment model. Int. J. Financ. Econ. 26(4), 5659–5665 (2021). https://doi.org/10.1002/ijfe.2086
Ahmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M.A., Mahmoodi, A., Amini Jam, P.: New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the support vector machine and heuristic algorithms of imperialist competition and genetic. Expert Syst. with Appl. 94, 21–31 (2018). https://doi.org/10.1016/j.eswa.2017.10.023
Mehrjoo, S., Jasemi, M., Mahmoudi, A.: A new methodology for deriving the efficient frontier of stocks portfolios: an advanced risk-return model. J. AI Data Mining. 2(2), 113–123 (2014). https://doi.org/10.22044/jadm.2014.305
Kumar, P., Panda, G.: Solving nonlinear interval optimization problem using stochastic programming technique. Opsearch 54, 752–765 (2017). https://doi.org/10.1007/s12597-017-0304-y
Kumar, P., Bhurjee, A.K.: An efficient solution of nonlinear enhanced interval optimization problems and its application to portfolio optimization. Soft. Comput. 25(7), 5423–5436 (2021). https://doi.org/10.1007/s00500-020-05541-z
Kumar, P., Behera, J., Bhurjee, A.: Solving mean-VaR portfolio selection model with interval-typed random parameter using interval analysis. Opsearch 59(1), 41–77 (2022). https://doi.org/10.1007/s12597-021-00531-7
Roy, P., Panda, G., Qiu, D.: Gradient-based descent linesearch to solve interval-valued optimization problems under Gh-differentiability with application to finance. J. Comput. Appl. Math. 436, 115402 (2024). https://doi.org/10.1016/j.cam.2023.115402
Sahu, B., Bhurjee, A., Kumar, P.: Efficient solutions for vector optimization problem on an extended interval vector space and its application to portfolio optimization. Expert Syst. Appl. 249, 123653 (2024). https://doi.org/10.1016/j.eswa.2024.123653
Best, M.J., Grauer, R.R.: On the sensitivity of mean-variance-efficient portfolios to changes in asset means: some analytical and computational results. Rev. Financ. Stud. 4(2), 315–342 (1991)
Gourieroux, C., Laurent, J., Scaillet, O.: Sensitivity analysis of values at risk. J. Emp. Financ. 7(3), 225–245 (2000). https://doi.org/10.1016/S0927-5398(00)00011-6
Guigues, V.: Sensitivity analysis and calibration of the covariance matrix for stable portfolio selection. Comput. Optim. Appl. 48(3), 553–579 (2011). https://doi.org/10.1007/s10589-009-9260-7
DeMiguel, V., Garlappi, L., Uppal, R.: Optimal versus Naive diversification: how inefficient is the 1/n portfolio strategy? Rev. Financ. Stud. 22(5), 1915–1953 (2009)
Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88(2), 365–411 (2004). https://doi.org/10.1016/S0047-259X(03)00096-4
Palczewski, A., Palczewski, J.: Theoretical and empirical estimates of mean-variance portfolio sensitivity. Eur. J. Operat. Res. 234(2), 402–410 (2014). https://doi.org/10.1016/j.ejor.2013.04.018
Michaud, R.O., Michaud, R.O.: Efficient asset management a practical guide to stock portfolio optimization and asset allocation. Oxford University Press, (2008)
Chopra, V.K., Ziemba, W.T.: The effect of errors in means, Variances, and Covariances on Optimal Portfolio Choice, World Scientific, (2013), Ch. 21, pp. 365–373. ar**v:https://www.worldscientific.com/doi/pdf/10.1142/9789814417358_0021, https://doi.org/10.1142/9789814417358_0021. https://www.worldscientific.com/doi/abs/10.1142/9789814417358_0021
Brunel, J.L., Idzorek, C.T.M., Mulvey, C.J.M.: Principles of asset allocation. Portfolio Management in Practice, Volume 1: Investment Management 1, 211 (2020)
Grauer, R.: Is the market Portfolio mean-variance efficient?, World Scientific, (2021), Ch. 47, pp. 1763–1787. ar**v:https://www.worldscientific.com/doi/pdf/10.1142/9789811202391_0047, https://doi.org/10.1142/9789811202391_0047. https://www.worldscientific.com/doi/abs/10.1142/9789811202391_0047
Paskaramoorthy, A., Woolway, M.: An empirical evaluation of sensitivity bounds for mean-variance portfolio optimisation. Financ. Res. Lett. 44, 102065 (2022). https://doi.org/10.1016/j.frl.2021.102065. (https://www.sciencedirect.com/science/article/pii/S154461232100146X)
Deng, X.-T., Li, Z.-F., Wang, S.-Y.: A minimax portfolio selection strategy with equilibrium. Eur. J. Operat. Res. 166(1), 278–292 (2005). https://doi.org/10.1016/j.ejor.2004.01.040
Wu, M., Kong, D.-W., Xu, J.-P., Huang, N.-J.: On interval portfolio selection problem. Fuzzy Optim. Decis. Making 12, 289–304 (2013). https://doi.org/10.1007/s10700-013-9155-z
Alefeld, G., Herzberger, J.: Introduction to interval computation. Academic press, UK (2012)
Hartman, D., Hladík, M., Ríha, D.: Computing the spectral decomposition of interval matrices and a study on interval matrix powers. Appl. Math. Comput. 403, 126174 (2021). https://doi.org/10.1016/j.amc.2021.126174
Singh, S., Panda, G.: SVD enclosure of a class of interval matrices. Inf. Sci. 666, 120386 (2024). https://doi.org/10.1016/j.ins.2024.120386
Singh, S., Panda, G.: Generalized eigenvalue problem for interval matrices. Arch. Math. 121(3), 267–278 (2023). https://doi.org/10.1007/s00013-023-01897-4
Singh, S., Panda, G.: Singular value decomposition of matrices with uncertain parameters, in. International Conference on Futuristic Technologies (INCOFT) 2022, pp. 1–5 (2022). https://doi.org/10.1109/INCOFT55651.2022.10094494
Rohn, J., Shary, S.P.: Interval matrices: regularity generates singularity. Linear Algebra Appl. 540, 149–159 (2018). https://doi.org/10.1016/j.laa.2017.11.020
Rohn, J., Farhadsefat, R.: Inverse interval matrix: a survey. Electron. J. Linear Algebra 22, 704–719 (2011). https://doi.org/10.13001/1081-3810.1468
Rohn, J.: Systems of linear interval equations. Linear Algebra Appl. 126, 39–78 (1989). https://doi.org/10.1016/0024-3795(89)90004-9
Rohn, J.: Positive definiteness and stability of interval matrices. SIAM J. Matrix Anal. Appl. 15(1), 175–184 (1994). https://doi.org/10.1137/S0895479891219216
Hager, W.: Applied numerical linear algebra, Classics in Applied Mathematics, Society for Industrial and Applied Mathematics, (2022). https://books.google.co.in/books?id=SDRZEAAAQBAJ
Hladik, M.: Optimal value bounds in nonlinear programming with interval data. TOP 19(1), 93–106 (2011). https://doi.org/10.1007/s11750-009-0099-y
Hladík, M., Daney, D., Tsigaridas, E.: Bounds on real eigenvalues and singular values of interval matrices. SIAM J. Matrix Anal. Appl. 31(4), 2116–2129 (2010). https://doi.org/10.1137/090753991
Rossi, M.: The capital asset pricing model: a critical literature review. Global Bus. Econ. Rev. 18(5), 604–617 (2016). https://doi.org/10.1504/GBER.2016.078682
Acknowledgements
The authors thank the anonymous reviewers for their careful reading and helpful comments, which greatly improved the paper.
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
Sarishti Singh: Conceptualization, Formal analysis, Methodology, Software, Validation, Original draft writing, Writing - Review & Editing. Geetanjali Panda: Formal analysis, Investigation, Methodology, Writing - Review & Editing, Supervision.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no Conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Singh, S., Panda, G. On the sensitivity of some portfolio optimization models using interval analysis. OPSEARCH (2024). https://doi.org/10.1007/s12597-024-00787-9
Accepted:
Published:
DOI: https://doi.org/10.1007/s12597-024-00787-9