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Dynamic portfolio allocation in goals-based wealth management

Author

Listed:
  • Sanjiv R. Das

    (Santa Clara University)

  • Daniel Ostrov

    (Santa Clara University)

  • Anand Radhakrishnan

    (Franklin Templeton Investments)

  • Deep Srivastav

    (Franklin Templeton Investments)

Abstract

We report a dynamic programming algorithm which, given a set of efficient (or even inefficient) portfolios, constructs an optimal portfolio trading strategy that maximizes the probability of attaining an investor’s specified target wealth at the end of a designated time horizon. Our algorithm also accommodates periodic infusions or withdrawals of cash with no degradation to the dynamic portfolio’s performance or runtime. We explore the sensitivity of the terminal wealth distribution to restricting the segment of the efficient frontier available to the investor. Since our algorithm’s optimal strategy can be on the efficient frontier and is driven by an investor’s wealth and goals, it soundly beats the performance of target date funds in attaining investors’ goals. These optimal goals-based wealth management strategies are useful for independent financial advisors to implement behavioral-based FinTech offerings and for robo-advisors.

Suggested Citation

  • Sanjiv R. Das & Daniel Ostrov & Anand Radhakrishnan & Deep Srivastav, 2020. "Dynamic portfolio allocation in goals-based wealth management," Computational Management Science, Springer, vol. 17(4), pages 613-640, December.
    • Handle: RePEc:spr:comgts:v:17:y:2020:i:4:d:10.1007_s10287-019-00351-7
    DOI: 10.1007/s10287-019-00351-7
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    References listed on IDEAS

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    Cited by:

    1. Denault, Michel & Simonato, Jean-Guy, 2022. "A note on a dynamic goal-based wealth management problem," Finance Research Letters, Elsevier, vol. 46(PB).
    2. Tessa Bauman & Bruno Gav{s}perov & Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar, 2023. "Deep Reinforcement Learning for Robust Goal-Based Wealth Management," Papers 2307.13501, arXiv.org.

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