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Simple Allocation Rules and Optimal Portfolio Choice Over the Lifecycle

Author

Listed:
  • Victor Duarte
  • Julia Fonseca
  • Aaron S. Goodman
  • Jonathan A. Parker

Abstract

In many areas of economics, relatively simple models developed for insight are used as quantitative guides to behavior. In this paper, we develop a machine-learning algorithm to solve for optimal portfolio choice in a complex, calibrated lifecycle model that includes many features of reality modeled only separately in previous work. The average optimal portfolio share invested in stocks declines with age, consistent both with age-dependent rules derived from simpler models and with implicit advice embedded in current lifecycle investment products. But optimal portfolios decline more evenly, slowly, and by less than in current lifecycle products, and have substantial heterogeneity, particularly by wealth level and expected equity premium, implying significant gains to further customization in these dimensions. Together these two factors imply gains from moving to optimal portfolios of up to 3.5 to 3.7 percent of average consumption.

Suggested Citation

  • Victor Duarte & Julia Fonseca & Aaron S. Goodman & Jonathan A. Parker, 2021. "Simple Allocation Rules and Optimal Portfolio Choice Over the Lifecycle," NBER Working Papers 29559, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29559
    Note: AP EFG TWP
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    Citations

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    Cited by:

    1. Marta Cota, 2023. "Extrapolative Income Expectations and Retirement Savings," CERGE-EI Working Papers wp751, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    2. Xianhua Peng & Steven Kou & Lekang Zhang, 2024. "A Machine Learning Algorithm for Finite-Horizon Stochastic Control Problems in Economics," Papers 2411.08668, arXiv.org, revised Dec 2024.
    3. Xianhua Peng & Wu Guo & Songyan Wang & Jianfei Zhu, 2026. "Deep Learning for Dynamic Programming with Recursive Utility Using First-order Conditions," Papers 2607.09461, arXiv.org.
    4. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
    5. Marlon Azinovic-Yang & Jan Zemlicka, 2025. "Deep Learning in the Sequence Space," CERGE-EI Working Papers wp802, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    6. Pavel Ciaian & Andrej Cupak & Pirmin Fessler & d’Artis Kancs, 2022. "Environmental and Social Preferences and Investments in Crypto-Assets," JRC Research Reports JRC129919, Joint Research Centre.
    7. Xianhua Peng & Wu Guo, 2026. "Deep Learning for Dynamic Programming with Recursive Utility," Papers 2607.04278, arXiv.org.
    8. Marlon Azinovic & Jan v{Z}emliv{c}ka, 2023. "Economics-Inspired Neural Networks with Stabilizing Homotopies," Papers 2303.14802, arXiv.org.
    9. Pavel Ciaian & Andrej Cupak & Pirmin Fessler & d'Artis Kancs, 2022. "Environmental-Social-Governance Preferences and Investments in Crypto-Assets (Pavel Ciaian, Andrej Cupak, Pirmin Fessler, d’Artis Kancs)," Working Papers 243, Oesterreichische Nationalbank (Austrian Central Bank).
    10. Marlon Azinovic-Yang & Jan v{Z}emliv{c}ka, 2025. "Deep Learning in the Sequence Space," Papers 2509.13623, arXiv.org, revised Mar 2026.

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D15 - Microeconomics - - Household Behavior - - - Intertemporal Household Choice; Life Cycle Models and Saving
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

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