We are taking a short break from our Safe Withdrawal Rate Series (see the latest post here) to look into some pretty fascinating data we came across the other day. There’s a small place on earth with rampant wealth inequality. If you had just one single dollar in your name you’d be worth more than the entire bottom 27% of the wealth distribution combined. The bottom half of the population owns only about 8.6% of all wealth, while the richest 10% own 40% of all wealth, and the richest 20% own about 62% of all wealth.
Despite the wealth inequality, there is surprising harmony. There’s no call for building walls. And no call for redistributing the “ill-gotten” profits of “evil capitalists” either. There is no envy! Folks in the lowest wealth bracket would regularly compliment their richer counterparts and say “Geez, you are rich. Good for you!”
Last week’s post about the Guyton-Klinger Dynamic Withdrawal Rule only scratched the surface and we ran out of time and space. So, today we like to present some additional and detailed simulation data to present at least four areas where Guyton and Klinger are quite confusing and misleading:
The ambiguity between withdrawal rates and withdrawal amounts. A casual reader might overlook the fact that the withdrawal amounts may very well fall outside a guardrail range. Inexplicably, Guyton and Klinger are very stingy with providing information on withdrawal amounts over time. There aren’t any time series charts of actual withdrawals in their paper.
True, Klinger shows time series charts in this paper, but they are only for the median retiree. Does anyone else see a problem with that? The good old 4% rule did splendidly for the median retiree since 1871 so I haven’t really learned anything by looking at the median. Wade Pfau showed (with a Monte-Carlo study) that the GK rule has a 10% chance of cutting withdrawals by 84% after 30 years. It’s very suspicious that the inventors of the rule don’t show more details about the distribution of withdrawals. You could call this either deception or invoke Hanlon’s Razor and blame it on sloppiness and incompetence, and both options are not very flattering.
The Guyton-Klinger rule (even with a 4% initial withdrawal rate) is very susceptible to equity valuations. Results look much worse if you look at the average past retiree with an elevated CAPE ratio (20-30).
Guyton-Klinger doesn’t afford you to miraculously increase your withdrawal amount without any drawback. The higher the initial withdrawal amount the higher the risk of massive spending cuts in the future.
The number one suggestion from readers for future projects in our Safe Withdrawal Rate Series: look into dynamic withdrawal rates, especially the Guyton-Klinger (GK) withdrawal rate rules. The interest in dynamic rate rules is understandable. Setting one initial withdrawal amount and then stubbornly adjusting it for CPI inflation regardless of what the portfolio does over the next 50-60 years seems wrong (despite the extremely simple and beautiful withdrawal rate arithmetic we pointed out last week).
So, here we go, our take on the dynamic withdrawal rates. Jonathan Guyton and William Klinger proposed a dynamic strategy that starts out just like the good old static withdrawal rate strategies, namely, setting one initial withdrawal amount and adjusting it for inflation. However, once the withdrawal rate (expressed as current withdrawal rate divided by the current portfolio value) wanders off too far from the target, the investor makes adjustments. Also, notice that this works both ways: You increase your withdrawals if the portfolio appreciated by a certain amount relative to your withdrawals and you decrease your withdrawals if the portfolio is lagging behind significantly. Think of this as guardrails on a road; you let the observed withdrawal rates wander off in either direction, for a while at least, but the guardrails prevent the withdrawal rate from wandering off too far, see chart below. It’s all pretty intuitive stuff, though, as we will see later, the devil is in the details.
Last week we published a Google-Sheet that calculates safe withdrawal rates to exactly match a specified real final asset value target. For 1,700+ retirement cohorts (starting between 1871 and 2015)! How do we compute those safe withdrawal rates in practice? I hope we don’t lose half of our subscribers this week but I thought it would be a great idea to show the mathematics behind our calculations. It’s simple arithmetic that we can easily implement in Excel/GoogleSheets and Octave/Matlab. But despite the simplicity, I haven’t seen anyone else use this methodology. Everybody (Trinity Study, cFIREsim, etc.) seems to be using the brute-force simulation technique of iterating portfolio values while applying withdrawals and returns over time. That’s an inefficient approach and we developed a more elegant technique. Continue reading “The Ultimate Guide to Safe Withdrawal Rates – Part 8: Technical Appendix”→