*A quick update on part 7 of this series, the Google Sheet toolkit to simulate your own safe withdrawal rate study: I added two more asset classes: Cash and Gold, with returns going all the way back to 1871. Enjoy!*

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.

So, let’s get cranking! We present another case study, the dreaded January 2000 retirement cohort, and also subject the Guyton-Klinger Rule to the whole ERN retirement withdrawal simulation engine to see how all the different retirement cohorts going back to 1871 would have fared. That’s over 1,700 cohorts because we insist on doing our simulations monthly, not annually.

### 2000-2016 case study

We use real, CPI-adjusted returns for stocks and bonds up to December 2016 and then extrapolate equity and bond returns the same way we described in our initial SWR post and in the 2000-2016 case study. If you don’t like that extrapolation exercise up to December 2019, feel free to ignore those data points.

In the chart below we plot the CPI-adjusted real portfolio values under the static 4% rule and the three different Guyton-Klinger rules. The same parameters as last week. The 4% rule looks pretty grim, having exhausted more than 50% of its initial value and quickly depleting more even if equities continue with 6.6% real returns for the next few years.

Not so the GK rules: The 4% rule is now back to over 80% and could as well grow back to 90% of the initial value. The GK-5% rule is hanging in there pretty well and only the GK-6% rule looks a little bit shaky, in that it’s stuck at only 60% of the initial real value and not able to recover even if returns are average the above average priced S&P500 index.

So far, so good. But just as last week, the time series of actual withdrawal amounts looks not so appetizing. Specifically, all of the GK rules experienced a 50%+ drop in their actual withdrawals and by the end of 2016, they are still between 30 and 40% below their initial withdrawal amount. What’s worse, even with pretty decent extrapolated returns going forward in 2017-2019, withdrawals are stuck at that reduced level.

So, Guyton-Klinger would have been a major letdown for the January 2000 retirement cohort. Especially the initial withdrawal rates above 4% would have caused large and permanent declines in real withdrawal amounts.

### 1871-2015 retirement cohorts: All CAPE regimes

Of course, we can go only so far with case studies, so we were curious how all the different retirees between 1871 and 2015 would have fared with the Guyton-Klinger rule. Probably better than the crazy worst case scenarios of 1966 and 2000, but how much better?

Let’s look at how the GK rule with a 4% initial withdrawal rate would have fared for retirees between 1871 and 2015. This is for all retirees regardless of initial CAPE Ratio. Also, we don’t want to show just the median but also some left tail stats, namely the minimum withdrawal, the 10th percentile and the 25th percentile. See chart below:

It turns out the median hardly sees any spending cut. The 25th percentile suffers a 20% cut and manages to recover back to 100% after 19 years. The 10th percentile sees a 40% cut and no recovery back to the initial CPI-adjusted amount within 30 years. Just for the record, I find the GK rule better than the static 4% rule because I’d rather cut my consumption by 40% with a 10% probability than run out of money with a 5% probability. But the tail risk scenarios are not appetizing. And we’re not talking about the 0.00001% tail event, but the 10% lower tail! I would consider myself quite risk averse and if the 10th percentile looks awful this rule would be a non-starter. Even folks who are less risk-averse should probably worry at least about the 25th percentile.

Last week we also pointed out the crucial distinction between real inflation-adjusted withdrawal amounts and the withdrawal rates. Withdrawal amounts can have wide swings, while withdrawal rates stay inside the Guyton-Klinger guardrails for the most part.

*(side note: There is one exception namely when a market move is large enough that even the x=0.10 adjustment will not take the observed withdrawal rate back inside the guardrails and it takes two months of adjustments to accomplish that. So, don’t be surprised to see a very small percentage of months with rates outside the guardrails.)*

In any case, let’s look at the distribution of (real) withdrawal amounts and withdrawal rates over the entire 360 months and all 1,700+ cohorts, see chart below. The top portion is what we really care about, how much we can consume, specifically the percentage of observations that fall into various buckets. The bottom portion is the less-informative figure because that % is multiplied by the current portfolio value, i.e., a moving target. Also, I added three dividers to split the buckets into four sections: Below the lower guardrail (<3.2% in this example), between the lower guardrail and the initial withdrawal rate (3.2-4.0%), between the initial withdrawal amount and the upper guardrail (4.0-4.8%) and above the upper guardrail (4.8%+).

The good news: Guyton-Klinger will likely generate higher withdrawal amounts than the initial. 48.1% of the time we’d even be above 1.2-times above the initial amount. That’s as expected because we already know that the naive 4% rule would have created massive over-accumulation of wealth and the GK rule simply harvests the excess gains.

But GK also forces your withdrawals to below the initial value with a significant probability. 15% probability to withdraw less than 80% of the initial (again, there ‘s no guardrail for the withdrawal *amounts*, only for the *rates*). 20.9% probability of falling into the 0.8 to 1.0-times the initial amount.

### 1871-2015 retirement cohorts: CAPE between 20 and 30

As we pointed out in our post on equity valuations, it’s risky to average over all equity valuation regimes when we already know that today we’re in a world of much more expensive equities relative to earnings. So, let’s run the 4%-GK rule only in the months when we had Shiller CAPE ratios of between 20 and 30 (Current CAPE is at 28!).

The chart with the withdrawal amounts doesn’t look so appealing anymore. Sure, the median is hanging in there pretty well; it dips slightly below 4, but recovers by year 16 only to increase substantially after that to more than 50% above the initial withdrawal amount by year 30. Guyton-Klinger did exactly what it was designed to do: scale up the withdrawals when the market cooperates.

But the less fortunate cohorts do much worse. The 25th percentile suffers about two decades of 25% decline of purchasing power. The 10th percentile drops to around 50% below for an entire decade. Of course, both recover back to their original withdrawal amounts but only after 26 and 29 years after retirement, respectively. Ouch!

The same distribution chart as before, see below. You now have a higher than 50% chance of consuming less than the original amount, even a 23.5% percent chance of consuming less than 0.8-times the original amount. All the while, of course, the withdrawal rates stay nicely inside the GK guardrails.

### What if we increase the withdrawal rate to 5%?

Well, the GK rule was invented to increase the initial withdrawal rate, so let’s see what happens when we push the initial annualized withdrawal to 5% of the portfolio. In the chart below we see that not even the median withdrawal amount can keep up. It drops by a moderate amount, 18% below the initial but it takes almost a quarter century to get back to the initial withdrawal amount. Now even the 25th percentile faces a 50% drop in withdrawals and only recovers after 30 years. The 10th percentile saw a close to 60% drop in consumption and no recovery within 30 years. Not a pretty picture.

The same distribution chart as above, but now looking even grimmer. We spend about one-third of the time withdrawing less than 0.8 times the initial amount, one-third of the time between 0.8 and 1.0 times the initial amount and another one-third above the “promised” 5%. Not a very pretty picture. Does anyone still claim that we can hack our initial withdrawal rate to 5% or more in today’s CAPE regime?

### Another reason we’re no fans of Guyton-Klinger: It’s a “**dumb”** rule

Ok, to be sure, I’m not saying that Messrs. Guyton and Klinger or the folks who are using the rule are dumb. I’m sure they are all very smart individuals. I’m just saying that the GK method leaves me just as clueless about what’s a safe and appropriate withdrawal rate than before I started working on this topic. We saw above that the rule is quite susceptible to the equity valuation regime (just like the dumb static 4% rule).

To see how fundamentally “dumb” the GK rule actually is, let’s try to do the following thought experiment. Imagine we start GK with a withdrawal rate that’s clearly way too low, say 2% p.a. Nobody has ever depleted a portfolio with such a low target withdrawal rate. In fact, even your truly, your crazy cranky Uncle Ern curmudgeon would not argue for a withdrawal rate lower than 2%. Why would anyone cut the withdrawals below the initial amount? It’s completely uncalled for? But that’s what happens in a non-trivial percentage of cohorts, see chart below:

True, you’d eventually withdraw much more. There’s a close to 50% chance of a 60%+ rise in the withdrawal amount, but you’d also have a 20% chance of withdrawals falling below the initial amount. That’s unnecessary!

### Conclusion

For full disclosure, we say it again: We like the Guyton-Klinger Rule slightly better than a naive static withdrawal policy like the 4% rule. But Guyton-Klinger suffers from the exact same problems as the 4%: It’s not safe. You replace the small risk of running out of money with the 4% rule with a moderate risk of large spending cuts throughout many years of your retirement with Guyton-Klinger.

As if Guyton-Klinger wasn’t already bad enough with a 4% initial withdrawal rate, jacking up the initial withdrawal rate to 5% or more as GK recommend, especially in today’s environment of expensive equities and low bond yields, would be particularly irresponsible. It’s a bit like sending a novice skier down a double black diamond slope. The helmet (=equivlanet of the guardrails) will likely ensure the poor guy won’t kill himself, but it won’t be a pleasant ride. For us, a dynamic withdrawal rule would have to be a lot smarter than Guyton-Klinger!

### We hope you enjoyed this week’s post! Please leave your comments below! Until next week!

- Part 1: Introduction
- Part 2: Some more research on
**capital preservation vs. capital depletion** - Part 3: Safe withdrawal rates in different
**equity valuation**regimes - Part 4: The impact of
**Social Security benefits** - Part 5: Changing the
**Cost-of-Living Adjustment**(COLA) assumptions - Part 6: A case study: 2000-2016
- Part 7: A
**DIY withdrawal rate toolbox**(via Google Sheets) - Part 8: A
**Technical appendix** - Part 9:
**Dynamic**withdrawal rates (Guyton-Klinger) - Part 10: Debunking Guyton-Klinger some more
- Part 11: Six criteria to grade dynamic withdrawal rules
- Part 12: Six reasons to be suspicious about the “
**Cash Cushion**“ - Part 13: Dynamic Stock-Bond Allocation through
**Prime Harvesting**

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Wow, interesting analysis. Beautiful charts! But if you don’t like the regular 4%-rule and Guyton-Klinger is no good what are the alternatives? What would you recommend in retirement? What would you do in your own retirement and what do you recommend for others who are maybe not as tech savvy as you?

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Thanks! Great question! I would start with a slightly lower withdrawal rate, say 3.5% and not 4%. I also the rule based on the Shiller CAPE earnings yield. First, it actually gives you some guidance as to what the initial withdrawal rate should be and then subsequently, it tells you how to adjust the withdrawal amounts in response to market swings.

One caveat, though: Right now the Shiller earnings yield is very low. So using a widely used rule 1%+0.5*1/CAPE would give you a measly withdrawal rate of only 2.8%. Maybe play with the parameters on cfirecim and see which parameterization gives you an acceptable initial withdrawal rate and has relatively stable payouts in the simulation.

And: needless to say, we will do a post on the CAPE rule in the future!

Cheers!

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ERN, great work once again.

In respect of the results published by Guyton Klinger and the lack of critical detail, as an alternative explanation to Hanlon’s Razor I would submit Occam’s Razor; the simple explanation being that if the guys that developed the model benefit commercially from having their names associated with it, then data that undermines their case would be inconvenient. So all we need ask is how likely is it that they would have found similar results during their tests as you found from yours. If they didn’t, then their tests were worryingly inadequate, if they did, then their paper was selective in its reporting of the results. Not great however you look at it, is it?

Thank you for putting the GK model to an independent test.

After reading your previous analysis I tested simple SWR and Guyton Klinger rules in Excel using expected returns expectations and standard deviation and running Monte Carlo analysis thereon. The results confirm your findings.

Also thank you for including gold and cash as asset classes in your spreadsheet – a great enhancement.

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Hi WN! Thanks! That’s exactly my thinking: There is no good alternative: either they didn’t think about the distribution or they did but didn’t show it. Hey, if it brings business to their firm, why bother to mention the side effects. 🙂

That’s great that you replicated the large drawdowns in a Monte Carlo study. Good to know that two methods yield similar results!!!

Have fun with the Gold and Cash asset returns. I played around a bit and found that gold can improve the fail-safe WRs. A little bit. At the margin. Gold worked well both in the 70s and the 2000s. Definitely food for thought!

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Thx again for doing all this great analysis.

And the discussions in the comments add a lot of value as well. I look forward to alternative methods.

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Awesome! Glad you liked the series. More to come in the next few weeks!

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Very complete and thorough analysis! I loved it. Actually made a similar one a long time ago, but it wasn’t this detailed 😉

Keep up!

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Awesome! Glad you liked it. Thanks for confirming you got the same results!!!

Cheers!

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[…] 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 […]

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[…] let’s move on to part 11. In our previous posts (Part 9 and Part 10), we wrote about the Guyton-Klinger dynamic withdrawal rule and why we’re not great fans. […]

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