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Revisiting the 4% Withdrawal Rule Using Monte Carlo Simulations with Random Market Declines

Nabil Tamimi et al.

Financial Planning Research Journal2024https://doi.org/10.2478/fprj-2024-0001article
ABDC C
Weight
0.41

What the paper says

Abstract This paper tracks the performance of a hypothetical retirement portfolio valued at $1,000,000 by applying the popular 4% rule of thumb withdrawals. These withdrawals are adjusted annually to account for simulated inflation and market return rates. Additionally, we incorporate different market shocks that resemble “black swan” events into our analysis. Commencing at a retirement age of 64 and adopting a 30-year retirement planning horizon, we employ Monte Carlo simulations to compute the final value of the portfolio at age 93 under various market shocks. These events occur randomly within the 30-year planning horizon. The average ending portfolio balance and the probability of fund depletion are reported, considering diverse portfolios with a range of asset allocations divided between stocks and bonds. The study's results demonstrate that a portfolio with a higher allocation to equity can yield a superior average ending portfolio balance while reducing the risk of fund depletion.

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https://doi.org/https://doi.org/10.2478/fprj-2024-0001

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@article{nabil2024,
  title        = {{Revisiting the 4% Withdrawal Rule Using Monte Carlo Simulations with Random Market Declines}},
  author       = {Nabil Tamimi et al.},
  journal      = {Financial Planning Research Journal},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.2478/fprj-2024-0001},
}

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Revisiting the 4% Withdrawal Rule Using Monte Carlo Simulations with Random Market Declines

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.26 × 0.4 = 0.10
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.