IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v99y2021ics0305048318312258.html
   My bibliography  Save this article

Benchmarking project portfolios using optimality thresholds

Author

Listed:
  • Korotkov, Vladimir
  • Wu, Desheng

Abstract

Risk assessment and selection of project portfolios are carried out under uncertainty, since this process uses historical data that can be adjusted in the future. The problem is whether the decision is still favorable and the level of risk is still acceptable to the investor. Assessing the quality of alternatives provides additional information about robustness to any changes in the parameters of the problem. The paper describes the concept of accuracy function. Using this concept, portfolios are evaluated to determine which portfolio is more robust with a possible increase in the level of risk. When the risk is reduced, the accuracy function indicates the optimality threshold when the selected portfolio can become Pareto optimal. This helps the investor to better assess the market situation and make more rational investment decisions. Based on the global risk assessment from the World Economic Forum report the case study describes the use of the accuracy function in assessing investment portfolios of projects participating in the Belt and Road initiative. The results show improvement paths to make economic arias more investment-friendly.

Suggested Citation

  • Korotkov, Vladimir & Wu, Desheng, 2021. "Benchmarking project portfolios using optimality thresholds," Omega, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:jomega:v:99:y:2021:i:c:s0305048318312258
    DOI: 10.1016/j.omega.2019.102166
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048318312258
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2019.102166?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. Schlag, Karl H. & Zapechelnyuk, Andriy, 2017. "Dynamic benchmark targeting," Journal of Economic Theory, Elsevier, vol. 169(C), pages 145-169.
    2. Xing, Hao, 2017. "Stability of the exponential utility maximization problem with respect to preferences," LSE Research Online Documents on Economics 57213, London School of Economics and Political Science, LSE Library.
    3. Ilya Molchanov & Ignacio Cascos, 2016. "Multivariate Risk Measures: A Constructive Approach Based On Selections," Mathematical Finance, Wiley Blackwell, vol. 26(4), pages 867-900, October.
    4. Mavrotas, George & Figueira, José Rui & Siskos, Eleftherios, 2015. "Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection," Omega, Elsevier, vol. 52(C), pages 142-155.
    5. Nikulin, Y. & Karelkina, O. & Mäkelä, M.M., 2013. "On accuracy, robustness and tolerances in vector Boolean optimization," European Journal of Operational Research, Elsevier, vol. 224(3), pages 449-457.
    6. Bilge Bozkurt & John W. Fowler & Esma S. Gel & Bosun Kim & Murat Köksalan & Jyrki Wallenius, 2010. "Quantitative Comparison of Approximate Solution Sets for Multicriteria Optimization Problems with Weighted Tchebycheff Preference Function," Operations Research, INFORMS, vol. 58(3), pages 650-659, June.
    7. Vincent Guigues, 2011. "Sensitivity analysis and calibration of the covariance matrix for stable portfolio selection," Computational Optimization and Applications, Springer, vol. 48(3), pages 553-579, April.
    8. Guasoni, Paolo & Muhle-Karbe, Johannes & Xing, Hao, 2017. "Robust portfolios and weak incentives in long-run investments," LSE Research Online Documents on Economics 60577, London School of Economics and Political Science, LSE Library.
    9. Caccioli, Fabio & Shrestha, Munik & Moore, Cristopher & Farmer, J. Doyne, 2014. "Stability analysis of financial contagion due to overlapping portfolios," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 233-245.
    10. Marek Libura & Yury Nikulin, 2006. "Stability and accuracy functions in multicriteria linear combinatorial optimization problems," Annals of Operations Research, Springer, vol. 147(1), pages 255-267, October.
    11. Gorissen, Bram L. & Yanıkoğlu, İhsan & den Hertog, Dick, 2015. "A practical guide to robust optimization," Omega, Elsevier, vol. 53(C), pages 124-137.
    12. Barbati, Maria & Greco, Salvatore & Kadziński, Miłosz & Słowiński, Roman, 2018. "Optimization of multiple satisfaction levels in portfolio decision analysis," Omega, Elsevier, vol. 78(C), pages 192-204.
    13. Anant Mishra & Sidhartha R. Das & James J. Murray, 2016. "Risk, Process Maturity, and Project Performance: An Empirical Analysis of US Federal Government Technology Projects," Production and Operations Management, Production and Operations Management Society, vol. 25(2), pages 210-232, February.
    14. Iwaki, Hideki & Osaki, Yusuke, 2017. "Comparative statics and portfolio choices under the phantom decision model," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 1-8.
    15. Paolo Guasoni & Johannes Muhle-Karbe & Hao Xing, 2017. "Robust Portfolios And Weak Incentives In Long-Run Investments," Mathematical Finance, Wiley Blackwell, vol. 27(1), pages 3-37, January.
    16. Agostino Capponi & Lijun Bo, 2016. "Robust Optimization of Credit Portfolios," Papers 1603.08169, arXiv.org.
    17. Jornada, Daniel & Leon, V. Jorge, 2016. "Biobjective robust optimization over the efficient set for Pareto set reduction," European Journal of Operational Research, Elsevier, vol. 252(2), pages 573-586.
    18. Christian Lücken & Benjamín Barán & Carlos Brizuela, 2014. "A survey on multi-objective evolutionary algorithms for many-objective problems," Computational Optimization and Applications, Springer, vol. 58(3), pages 707-756, July.
    19. David P. Helmbold & Robert E. Schapire & Yoram Singer & Manfred K. Warmuth, 1998. "On‐Line Portfolio Selection Using Multiplicative Updates," Mathematical Finance, Wiley Blackwell, vol. 8(4), pages 325-347, October.
    20. David L. Olson & Desheng Dash Wu, 2017. "Enterprise Risk Management Models," Springer Texts in Business and Economics, Springer, edition 2, number 978-3-662-53785-5, August.
    21. Ignacio Cascos & Ilya Molchanov, 2013. "Multivariate risk measures: a constructive approach based on selections," Papers 1301.1496, arXiv.org, revised Jul 2016.
    22. Zhao, Yonggan, 2007. "A dynamic model of active portfolio management with benchmark orientation," Journal of Banking & Finance, Elsevier, vol. 31(11), pages 3336-3356, November.
    23. Hao Xing, 2017. "Stability Of The Exponential Utility Maximization Problem With Respect To Preferences," Mathematical Finance, Wiley Blackwell, vol. 27(1), pages 38-67, January.
    24. Aharon Ben-Tal & Dick den Hertog & Anja De Waegenaere & Bertrand Melenberg & Gijs Rennen, 2013. "Robust Solutions of Optimization Problems Affected by Uncertain Probabilities," Management Science, INFORMS, vol. 59(2), pages 341-357, April.
    25. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    26. Tadashi Dohi & Eiichi Kitaoka & Shunji Osaki, 1994. "Alternative optimality criteria of portfolio selection based upon threshold stopping rule," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 10(4), pages 257-268.
    27. Korotin, Vladimir & Popov, Victor & Tolokonsky, Andrey & Islamov, Rustam & Ulchenkov, Arseniy, 2017. "A multi-criteria approach to selecting an optimal portfolio of refinery upgrade projects under margin and tax regime uncertainty," Omega, Elsevier, vol. 72(C), pages 50-58.
    28. Sefair, Jorge A. & Méndez, Carlos Y. & Babat, Onur & Medaglia, Andrés L. & Zuluaga, Luis F., 2017. "Linear solution schemes for Mean-SemiVariance Project portfolio selection problems: An application in the oil and gas industry," Omega, Elsevier, vol. 68(C), pages 39-48.
    29. Nicholas G. Hall & Daniel Zhuoyu Long & Jin Qi & Melvyn Sim, 2015. "Managing Underperformance Risk in Project Portfolio Selection," Operations Research, INFORMS, vol. 63(3), pages 660-675, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yi, Changsheng & Chen, Zhaoming & Chen, Hongchen, 2023. "Opportunity knocks but just once: Impact of infrastructure investment decision on climate adaptation to flood events," Omega, Elsevier, vol. 121(C).

    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. Korotkov, Vladimir & Wu, Desheng, 2020. "Evaluating the quality of solutions in project portfolio selection," Omega, Elsevier, vol. 91(C).
    2. Shuo Gong & Yijun Hu & Linxiao Wei, 2022. "Risk measurement of joint risk of portfolios: a liquidity shortfall aspect," Papers 2212.04848, arXiv.org.
    3. Chassein, André & Goerigk, Marc, 2018. "Compromise solutions for robust combinatorial optimization with variable-sized uncertainty," European Journal of Operational Research, Elsevier, vol. 269(2), pages 544-555.
    4. Wang, Wei & Xu, Huifu & Ma, Tiejun, 2023. "Optimal scenario-dependent multivariate shortfall risk measure and its application in risk capital allocation," European Journal of Operational Research, Elsevier, vol. 306(1), pages 322-347.
    5. Fei Sun & Jingchao Li & Jieming Zhou, 2018. "Dynamic risk measures with fluctuation of market volatility under Bochne-Lebesgue space," Papers 1806.01166, arXiv.org, revised Mar 2024.
    6. Shunichi Ohmori, 2021. "A Predictive Prescription Using Minimum Volume k -Nearest Neighbor Enclosing Ellipsoid and Robust Optimization," Mathematics, MDPI, vol. 9(2), pages 1-16, January.
    7. Antonio Avilés López & José Miguel Zapata García, 2020. "Boolean Valued Representation of Random Sets and Markov Kernels with Application to Large Deviations," Mathematics, MDPI, vol. 8(10), pages 1-23, October.
    8. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2019. "Reverse sensitivity testing: What does it take to break the model?," European Journal of Operational Research, Elsevier, vol. 274(2), pages 654-670.
    9. Antonio J. Conejo & Nicholas G. Hall & Daniel Zhuoyu Long & Runhao Zhang, 2021. "Robust Capacity Planning for Project Management," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1533-1550, October.
    10. Ilya Molchanov & Anja Muhlemann, 2019. "Nonlinear expectations of random sets," Papers 1903.04901, arXiv.org.
    11. Colubi, Ana & Ramos-Guajardo, Ana Belén, 2023. "Fuzzy sets and (fuzzy) random sets in Econometrics and Statistics," Econometrics and Statistics, Elsevier, vol. 26(C), pages 84-98.
    12. Chen, Yanhong & Hu, Yijun, 2017. "Set-valued risk statistics with scenario analysis," Statistics & Probability Letters, Elsevier, vol. 131(C), pages 25-37.
    13. Xiaochuan Deng & Fei Sun, 2019. "Regulator-based risk statistics for portfolios," Papers 1904.08829, arXiv.org, revised Jun 2020.
    14. Andreas Haier & Ilya Molchanov & Michael Schmutz, 2015. "Intragroup transfers, intragroup diversification and their risk assessment," Papers 1511.06320, arXiv.org, revised Nov 2016.
    15. Haier Andreas & Molchanov Ilya, 2019. "Multivariate risk measures in the non-convex setting," Statistics & Risk Modeling, De Gruyter, vol. 36(1-4), pages 25-35, December.
    16. Yanhong Chen & Yijun Hu, 2019. "Set-Valued Law Invariant Coherent And Convex Risk Measures," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-18, May.
    17. Emmanuel Lepinette & Ilya Molchanov, 2017. "Conditional cores and conditional convex hulls of random sets," Papers 1711.10303, arXiv.org.
    18. Andreas Haier & Ilya Molchanov, 2019. "Multivariate risk measures in the non-convex setting," Papers 1902.00766, arXiv.org, revised Sep 2019.
    19. Roozegar, Roohollah & Balakrishnan, Narayanaswamy & Jamalizadeh, Ahad, 2020. "On moments of doubly truncated multivariate normal mean–variance mixture distributions with application to multivariate tail conditional expectation," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
    20. c{C}au{g}{i}n Ararat & Zachary Feinstein, 2019. "Set-Valued Risk Measures as Backward Stochastic Difference Inclusions and Equations," Papers 1912.06916, arXiv.org, revised Sep 2020.

    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:eee:jomega:v:99:y:2021:i:c:s0305048318312258. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    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.