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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
We develop a machine-learning solution algorithm to solve for optimal portfolio choice in a lifecycle model that includes many features of reality modelled only separately in previous work. We use the quantitative model to evaluate the consumption-equivalent welfare losses from using simple rules for portfolio allocation across stocks, bonds, and liquid accounts instead of the optimal portfolio choices, both for optimizing households and for households that undersave. We find that the consumption-equivalent losses from using an age-dependent rule as embedded in current target-date/lifecycle funds (TDFs) are substantial, around 2 to 3 percent of consumption, despite the fact that TDF rules mimic average optimal behavior by age closely until shortly before retirement. Optimal equity shares have substantial heterogeneity, particularly by wealth level, state of the business cycle, and dividend-price ratio, implying substantial gains to further customization of advice or TDFs in these dimensions.

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. Pavel Ciaian & Andrej Cupak & Pirmin Fessler & d'Artis Kancs, 2022. "Environmental-Social-Governance Preferences and the Holding of Crypto-Assets," EERI Research Paper Series EERI RP 2022/07, Economics and Econometrics Research Institute (EERI), Brussels.
    4. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
    5. 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.
    6. Marlon Azinovic & Jan v{Z}emliv{c}ka, 2023. "Economics-Inspired Neural Networks with Stabilizing Homotopies," Papers 2303.14802, arXiv.org.

    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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