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Optimal Entrepreneurial Decisions in a Completely Stochastic Environment

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  • Nils H. Hakansson

    (University of California, Berkeley)

Abstract

This paper develops a normative model of the entrepreneur's decision problem in which the following elements are stochastic: the entrepreneur's preferences, his lifetime, the returns from investments, and the process obeyed by the interest rate. Furthermore, the entrepreneur's preferences are assumed to be sensitive to the opportunities facing him at each decision point as well as other environmental factors. At each decision point the entrepreneur must decide how to allocate his resources between consumption, life insurance, various investment opportunities, and lending/borrowing. His objective is postulated to be the maximization of expected utility from consumption as long as he lives and from the bequest left upon his death. Optimal decision functions are obtained in closed form for a class of utility functions; their properties are examined and compared to those of the optimal strategies of less general models.

Suggested Citation

  • Nils H. Hakansson, 1971. "Optimal Entrepreneurial Decisions in a Completely Stochastic Environment," Management Science, INFORMS, vol. 17(7), pages 427-449, March.
    • Handle: RePEc:inm:ormnsc:v:17:y:1971:i:7:p:427-449
    DOI: 10.1287/mnsc.17.7.427
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    Cited by:

    1. Reza Keykhaei, 2020. "Portfolio selection in a regime switching market with a bankruptcy state and an uncertain exit-time: multi-period mean–variance formulation," Operational Research, Springer, vol. 20(3), pages 1231-1254, September.
    2. Mousa, A.S. & Pinheiro, D. & Pinto, A.A., 2016. "Optimal life-insurance selection and purchase within a market of several life-insurance providers," Insurance: Mathematics and Economics, Elsevier, vol. 67(C), pages 133-141.
    3. Scott A. Shane & Karl T. Ulrich, 2004. "50th Anniversary Article: Technological Innovation, Product Development, and Entrepreneurship in Management Science," Management Science, INFORMS, vol. 50(2), pages 133-144, February.
    4. Fenge Chen & Bing Li & Xingchun Peng, 2022. "Portfolio Selection and Risk Control for an Insurer With Uncertain Time Horizon and Partial Information in an Anticipating Environment," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 635-659, June.
    5. Tian Chen & Ruyi Liu & Zhen Wu, 2022. "Continuous-time mean-variance portfolio selection under non-Markovian regime-switching model with random horizon," Papers 2205.06434, arXiv.org.
    6. Blanchet-Scalliet, Christophette & El Karoui, Nicole & Jeanblanc, Monique & Martellini, Lionel, 2008. "Optimal investment decisions when time-horizon is uncertain," Journal of Mathematical Economics, Elsevier, vol. 44(11), pages 1100-1113, December.
    7. Onesun Steve Yoo & Charles J. Corbett & Guillaume Roels, 2016. "Optimal Time Allocation for Process Improvement for Growth-Focused Entrepreneurs," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 361-375, July.
    8. Huang, Zongyuan & Wang, Haiyang & Wu, Zhen, 2020. "A kind of optimal investment problem under inflation and uncertain time horizon," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    9. Lionel Martellini & Branko Uroševi'{c}, 2006. "Static Mean-Variance Analysis with Uncertain Time Horizon," Management Science, INFORMS, vol. 52(6), pages 955-964, June.
    10. Toda, Alexis Akira, 2014. "Incomplete market dynamics and cross-sectional distributions," Journal of Economic Theory, Elsevier, vol. 154(C), pages 310-348.
    11. Blanchet-Scalliet, Christophette & El Karoui, Nicole & Martellini, Lionel, 2005. "Dynamic asset pricing theory with uncertain time-horizon," Journal of Economic Dynamics and Control, Elsevier, vol. 29(10), pages 1737-1764, October.
    12. Fahmy, Hany, 2020. "Mean-variance-time: An extension of Markowitz's mean-variance portfolio theory," Journal of Economics and Business, Elsevier, vol. 109(C).
    13. Christian Dehm & Thai Nguyen & Mitja Stadje, 2020. "Non-concave expected utility optimization with uncertain time horizon," Papers 2005.13831, arXiv.org, revised Oct 2021.

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