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Contingent Portfolio Programming for the Management of Risky Projects

Author

Listed:
  • Janne Gustafsson

    (Systems Analysis Laboratory, Helsinki University of Technology, Otakaari 1M, P.O. Box 1100, 02015 HUT, Finland)

  • Ahti Salo

    (Systems Analysis Laboratory, Helsinki University of Technology, Otakaari 1M, P.O. Box 1100, 02015 HUT, Finland)

Abstract

Methods for selecting a research and development (R&D) project portfolio have attracted considerable interest among practitioners and academics. This notwithstanding, the industrial uptake of these methods has remained limited, partly because of the difficulties of capturing relevant concerns in R&D portfolio management. Motivated by these difficulties, we develop contingent portfolio programming (CPP), which extends earlier approaches in that it (i) uses states of nature to capture exogenous uncertainties, (ii) models resources through dynamic state variables, and (iii) provides guidance for the selection of an optimal project portfolio that is compatible with the decision maker’s risk attitude. Although CPP is presented here in the context of R&D project portfolios, it is applicable to a variety of investment problems where the dynamics and interactions of investment opportunities must be accounted for.

Suggested Citation

  • Janne Gustafsson & Ahti Salo, 2005. "Contingent Portfolio Programming for the Management of Risky Projects," Operations Research, INFORMS, vol. 53(6), pages 946-956, December.
    • Handle: RePEc:inm:oropre:v:53:y:2005:i:6:p:946-956
    DOI: 10.1287/opre.1050.0225
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    References listed on IDEAS

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    Cited by:

    1. Guilherme Vitolo & Flavio Cipparrone, 2014. "Strategic Implications Of Project Portfolio Selection," Accounting & Taxation, The Institute for Business and Finance Research, vol. 6(2), pages 11-20.
    2. Giovanna Lo Nigro & Azzurra Morreale & Lorenzo Abbate, 2016. "An Open Innovation Decision Support System to Select a Biopharmaceutical R&D Portfolio," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 37(6), pages 392-406, September.
    3. Bohnert, Alexander & Born, Patricia & Gatzert, Nadine, 2014. "Dynamic hybrid products in life insurance: Assessing the policyholders’ viewpoint," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 87-99.
    4. Jing Ai & Patrick L. Brockett & Tianyang Wang, 2017. "Optimal Enterprise Risk Management and Decision Making With Shared and Dependent Risks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(4), pages 1127-1169, December.
    5. Huang, Xiaoxia & Xiang, Lan & Islam, Sardar M.N., 2014. "Optimal project adjustment and selection," Economic Modelling, Elsevier, vol. 36(C), pages 391-397.
    6. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2019. "Portfolio optimization of safety measures for the prevention of time-dependent accident scenarios," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    7. Loukianova, A. & Nurullaev, D., 2018. "Evaluation of real options portfolio for investment projects," Working Papers 15114, Graduate School of Management, St. Petersburg State University.
    8. Liesiö, Juuso & Salo, Ahti, 2012. "Scenario-based portfolio selection of investment projects with incomplete probability and utility information," European Journal of Operational Research, Elsevier, vol. 217(1), pages 162-172.
    9. Janne Gustafsson, 2020. "Valuation of Research and Development Projects Using Buying and Selling Prices: Generalized Definitions," Decision Analysis, INFORMS, vol. 17(2), pages 154-168, June.
    10. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Nei Yoshihiro Soma & Carlos Eduardo Sanches da Silva, 2021. "MCDM-Based R&D Project Selection: A Systematic Literature Review," Sustainability, MDPI, vol. 13(21), pages 1-34, October.
    11. Salo, Ahti & Andelmin, Juho & Oliveira, Fabricio, 2022. "Decision programming for mixed-integer multi-stage optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 299(2), pages 550-565.
    12. Milford, James & Henrion, Max & Hunter, Chad & Newes, Emily & Hughes, Caroline & Baldwin, Samuel F., 2022. "Energy sector portfolio analysis with uncertainty," Applied Energy, Elsevier, vol. 306(PA).
    13. Liesiö, Juuso & Xu, Peng & Kuosmanen, Timo, 2020. "Portfolio diversification based on stochastic dominance under incomplete probability information," European Journal of Operational Research, Elsevier, vol. 286(2), pages 755-768.
    14. Liesiö, Juuso & Kallio, Markku & Argyris, Nikolaos, 2023. "Incomplete risk-preference information in portfolio decision analysis," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1084-1098.
    15. Solak, Senay & Clarke, John-Paul B. & Johnson, Ellis L. & Barnes, Earl R., 2010. "Optimization of R&D project portfolios under endogenous uncertainty," European Journal of Operational Research, Elsevier, vol. 207(1), pages 420-433, November.

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