Monte Carlo-Based Retirement Spending Strategies — Oblivious Investor

I’ve written before about how I consider retirement spending strategies to exist on a spectrum.

• Strategies at one end of the spectrum (e.g., the classic “4% rule” approach) do not adjust spending based on portfolio performance. This makes spending predictable, but the tradeoff is that it results in both a higher probability of portfolio depletion as well as a higher probability of having a huge unspent sum at death. In other words, by not adjusting spending based on portfolio performance, you have a greater likelihood of ultimately overspending or underspending.
• And at the other end of the spectrum is a strategy in which you spend a given percentage of the portfolio each year. By adjusting spending based on the portfolio’s performance (e.g., the portfolio fell by 30% last year, so this year’s spending will be 30% lower than last year’s spending), you dramatically reduce the risk of underspending or overspending. But for some people, the necessary changes to spending from one year to the next simply would not be plausible.

For most people, a strategy somewhere in the middle is going to make the most sense (i.e., adjust spending somewhat over time, but don’t necessarily increase/decrease spending by a full 30% in a given year if the portfolio grew/fell by 30% in the year before).

For people who use Monte Carlo simulations as a part of their retirement planning, Derek Tharp and Justin Fitzpatrick recently shared an approach that may be of interest:

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Thanks for reading!