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Optimal Sequential Investment Decisions Under Conditions of Uncertainty

Author

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  • Gregory P. Prastacos

    (University of Pennsylvania and The Athens School of Economics and Business Science, Athens, Greece)

Abstract

This paper presents a mathematical model of sequential investment behavior under conditions of uncertainty. The model addresses the problem of an investor with access to a limited pool of capital, who makes sequential decisions on long-lasting investments, under uncertainty as to the timing or the quality of future opportunities. We derive optimal investment strategies for the cases where the return from investment is a convex or concave function, we present closed form solutions for commonly adopted return functions, and we evaluate how the optimal investment behavior should change when changes occur in the environment, or the underlying probability distributions. In addition, we analyze three modifications of the problem. The results presented in this paper extend previous results on investment behavior for long-lasting (irreversible) decisions; in addition, some results are in accordance with existing ones from portfolio theory and/or search theory.

Suggested Citation

  • Gregory P. Prastacos, 1983. "Optimal Sequential Investment Decisions Under Conditions of Uncertainty," Management Science, INFORMS, vol. 29(1), pages 118-134, January.
    • Handle: RePEc:inm:ormnsc:v:29:y:1983:i:1:p:118-134
    DOI: 10.1287/mnsc.29.1.118
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    Cited by:

    1. Gordon Burtch & Diwakar Gupta & Paola Martin, 2021. "Referral Timing and Fundraising Success in Crowdfunding," Manufacturing & Service Operations Management, INFORMS, vol. 23(3), pages 676-694, May.
    2. Heidenberger, Kurt, 1996. "Dynamic project selection and funding under risk: A decision tree based MILP approach," European Journal of Operational Research, Elsevier, vol. 95(2), pages 284-298, December.
    3. Grace Y. Lin & Yingdong Lu & David D. Yao, 2008. "The Stochastic Knapsack Revisited: Switch-Over Policies and Dynamic Pricing," Operations Research, INFORMS, vol. 56(4), pages 945-957, August.
    4. Xuhan Tian & Junmin (Jim) Shi & Xiangtong Qi, 2022. "Stochastic Sequential Allocations for Creative Crowdsourcing," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 697-714, February.
    5. So, Mee Chi & Thomas, Lyn C. & Huang, Bo, 2016. "Lending decisions with limits on capital available: The polygamous marriage problem," European Journal of Operational Research, Elsevier, vol. 249(2), pages 407-416.
    6. Alessandro Arlotto & Noah Gans & J. Michael Steele, 2014. "Markov Decision Problems Where Means Bound Variances," Operations Research, INFORMS, vol. 62(4), pages 864-875, August.
    7. Anton J. Kleywegt & Jason D. Papastavrou, 1998. "The Dynamic and Stochastic Knapsack Problem," Operations Research, INFORMS, vol. 46(1), pages 17-35, February.
    8. Asa B. Palley & Mirko Kremer, 2014. "Sequential Search and Learning from Rank Feedback: Theory and Experimental Evidence," Management Science, INFORMS, vol. 60(10), pages 2525-2542, October.
    9. Anton J. Kleywegt & Jason D. Papastavrou, 2001. "The Dynamic and Stochastic Knapsack Problem with Random Sized Items," Operations Research, INFORMS, vol. 49(1), pages 26-41, February.
    10. Gross, Eitan, 2016. "On the Bellman’s principle of optimality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 217-221.
    11. Yuanzheng Ma & Tong Wang & Huan Zheng, 2023. "On fairness and efficiency in nonprofit operations: Dynamic resource allocations," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1778-1792, June.

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