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.

The Wall Street Journal calls this methodology “A Better Way to Tap Your Retirement Savings” because it allows *higher *(!) withdrawal rates than the traditional 4% rule. As you probably know by now, we’re no fans of the 4% rule and if people claim that we can push the envelope even further by just applying some “magic dynamic” we are very suspicious. Specifically, we believe that the GK methodology has (at least) one flaw and we like to showcase it here.

### Guyton-Klinger basics

See a nice summary here and the original paper here. An interesting link with lots of calculations, examples and an Excel Spreadsheet with sample calculations is here. cFIREsim also simulates the GK method! In any case, the Guyton-Klinger method has four major ingredients, of which three are essential and the fourth seems to be there mostly for “cosmetic” reasons:

- Forego the CPI-adjustment in withdrawals when the nominal portfolio return was negative. Even when doing the CPI-adjustment following a positive return, cap it at 6%, which seems somewhat arbitrary to us.
- (Guard Rail 1) If the withdrawal rate (current withdrawal amount divided by current portfolio value) is greater than 1.2 times the initial withdrawal rate then cut the withdrawal amount by 10%.
- (Guard Rail 2) If the withdrawal rate (current withdrawal amount divided by current portfolio value) is smaller than 0.8 times the initial withdrawal rate then increase the withdrawal amount by 10%.
- Some pretty convoluted mumbo-jumbo on the withdrawal mechanics, e.g., which assets to draw down first, a process they call the
**Portfolio Management Rule**. To us, this seems like a slightly infantile description of a portfolio rebalance back to target weights, i.e., draw down the assets with the highest returns first because they are the ones with the largest overweights relative to the target weights. Why not just do a simple rebalance to target weights then? There are only two possibilities: a) There is**no gain**from their procedure relative to a plain rebalance, then why do it the complicated way? b) There**is an advantage**relative to a simple rebalance but given the ad-hoc nature of their rules, we would argue that any advantage is likely a fluke. In fact, by GK’s own admission (Table 2 in their paper), their portfolio management rule doesn’t add anything when targeting a 90% probability of success and adds only marginally when targeting a 95% probability of success.

### The way we model the dynamic rule is a simplified (decluttered) version of Guyton-Klinger:

- Run simulations at a
**monthly**frequency, rather than annual, to be consistent with our other research on the topic and, of course, for the plain and simple reason that once we are retired we don’t like a whole year worth of withdrawals sitting around in cash every January. We hate leaving money on the table, as you may know from our post on emergency funds. - Since we don’t have all the different equity asset class returns going back to 1871 we simply assume that there is one single equity index (U.S. Large Cap) and one single bond asset (10-year Benchmark U.S. Treasury Bond) as in our previous research, again consistent with our earlier research based on a simple Stock-Bond portfolio
- We discard GK’s convoluted portfolio management rule. We have only two assets (stocks and bonds) and simply assume that the portfolio is rebalanced back to the target weights every month. It’s simpler to model and calculate in our number-crunching software: a simple matrix algebra operation, i.e., we multiply the Tx2 matrix of stock/bond returns with a 2×1 vector of asset weights. Done! No need to carry around time-varying portfolio weights.
- If the 12-month trailing (real) return was negative, then forego the inflation adjustment, i.e., shrink the real withdrawal by the CPI-rate that month. If the 12-month trailing return was positive, then do the CPI-adjustment. We don’t use the Guyton-Klinger 6% cap on the CPI-adjustment, which seems pretty arbitrary and also causes a big loss of purchasing power in the 1970s.
- If the withdrawal rate (current withdrawal amount divided by current portfolio value) is greater than (1+g) times the initial withdrawal rate then cut the withdrawal amount by x. It’s the same setup as in Guyton-Klinger.
- If the withdrawal rate (current withdrawal amount divided by current portfolio value) is smaller than (1-g) times the initial withdrawal rate then increase the withdrawal amount by x. Again, the same as in Guyton-Klinger.

Our take on Guyton-Klinger captures the main ingredients: the guardrails and a decision rule for making vs. skipping the CPI-adjustments, without the baggage of their complicated and likely useless portfolio management rule.

### Results

Let’s start with the **good news**. The number one reason we like the GK-rule: If done right it’s (almost) impossible to run out of money with the GK rule (in very stark contrast to the non-trivial probabilities of depleting the portfolio under the naive static withdrawal rule, see our previous research). You heard that right! Our simulations show that if we set the initial withdrawal not too crazy high and we use a tight enough guard rail parameter (g=20%) and aggressive enough adjustment parameter (x=10%) then even under adverse market conditions (e.g., the January 1966 retirement cohort) we won’t run out of money. *(side note: this requires to do the guardrail adjustments throughout retirement, while GK stop doing the adjustments 15 years before the end of the retirement horizon, in which case you do face the risk of running out of money)*

Now for the bad news. We identified one reason to be skeptical, very skeptical, about the Guyton-Klinger rule:

### Under Guyton-Klinger you may have to curb your consumption. **By a lot more than you think!**

Let’s make this more fun and let me first present the GK simulation results in a very deceptive way to make the dynamic GK rules appear much better than they really are. Let’s see who can spot the deception…

Let’s present a **1966 case study**, the last time in recent history when the 4% rule failed (though you may remember our 2000-2016 case study, where we showed that the 4% rule also looks pretty shaky for the January 2000 retirement cohort). If Guyton-Klinger can succeed here it will succeed almost anywhere! Throughout, we assume an 80%/20% Stock/Bond portfolio and the same return assumptions as outlined in part 1 of this series. We consider 4 different withdrawal strategies:

- The good old 4% rule: set the initial monthly withdrawal rate to 0.333% (=4% p.a.) and then adjust the withdrawals by CPI regardless of the portfolio performance. This method depletes the portfolio after 28 years.
- Guyton-Klinger with +/-20% guardrails and 10% adjustments and a 4% p.a. initial withdrawal rate
- Same as 2, but with a 5% initial withdrawal rate
- Same as 2, but with a 6% (!) initial withdrawal rate

The time series chart of the real, CPI-adjusted portfolio value (normalized to 100 in January 1966) is below:

Holy Mackerel!!! GK beats the 4% rule and it’s not even close. The GK-4% has surpassed the initial $100 (adjusted for CPI!) after 26 years while the old 4% has gone bankrupt after 28 years. The 5% rule is almost back to normal and the 6% rule is hanging in there pretty well, too. Talking about withdrawal percentages, let’s look at those as well, see picture below:

Amazing! Look at the 5% Guyton-Klinger rule. By construction, it stays between 4% and 6% (=5% times 1+0.2 and 1-0.2, respectively), so it never falls below 4% due to the guardrails. Moreover, it has a higher initial withdrawal and a higher final value! It appears to beat the static 4% withdrawal rate in *every* dimension we care about. It looks like the occasional 10% cuts in withdrawals haven’t hurt us too much. Amazing! Have we just found a Safe Withdrawal Rate Nirvana? Let’s nominate Guyton and Klinger for the Nobel Prize! Economics or Peace? Heck, both, of course, and in the same year to save them the travel expenses to Stockholm!

But before you open the champagne bottles, let’s bring us all back to planet earth. **I just scammed you all!** To be sure, the numbers are 100% correct, but the way I presented them was false advertising, even borderline fraudulent.

**Where was the deception I mentioned above?**

Pay close attention to what I **didn’t show** you yet! I never showed you the actual inflation-adjusted withdrawal **amounts**. Who cares about **percentages** of the portfolio value when the portfolio value is a moving target? I want to know the **dollar amounts**. It’s called “Show me the **money**” and not “Show me the **percentages**,” after all. So, how much in CPI-adjusted dollars can I withdraw under the different rules and, specifically, by how much do I have to curb my consumption during retirement due to the withdrawal cuts once we hit the guard rails? That’s displayed in the chart below:

What a disappointment! That’s where the Guyton-Klinger skeletons are hidden. Sure, when your initial withdrawal rate is 5% you never drop below a 4% withdrawal *rate* (due to the guardrail), but it’s 4% of a much-depleted portfolio value, not 4% of the *initial value*. That subtle distinction makes a huge difference. For example, the average withdrawal values for GK under the 4/5/6% initial withdrawal rates are only 2.74%, 3.02%, and 3.22% of the initial portfolio value, respectively. Well, it’s no longer a surprise that we have a higher final value than under the static 4% rule because we withdrew so much less! The advertised 5% withdrawal was only 3.02% withdrawal. What a scam!

Talking about skeletons, here’s more data from the GK horror show: The decline of withdrawals from peak to bottom is a staggering 59%, 66%, and 69%, respectively. Ouch! If you thought that the $1,000,000 portfolio can afford you a $50,000 per year lifestyle using GK, you better plan for a few sub-$20k years and an entire decade (!) of sub-$25k p.a. withdrawals. Suddenly the Guyton-Klinger method doesn’t look so hot anymore.

How is it possible to experience such massive declines in the withdrawals? The GK-rules hide this drop behind the +/-20% guardrails and +/-10% withdrawal adjustments (not to mention the distraction in the form of the asinine “portfolio management rule”) that make it sound like we only suffer relatively minor and temporary decreases in purchasing power. But the 0.2 guardrail is **on top of the drop in the portfolio**. If the portfolio is down by 50% and you hit the lower guardrail, the drop in the withdrawal is (1-0.5)x(1-0.2)=0.4 = 60% under the initial withdrawal. Hence, the large reduction in withdrawals! Skipping the CPI-adjustment in some of the years also erodes the purchasing power.

The claim that we can afford a higher initial withdrawal rate than under the fixed withdrawal rules is a pretty blatant case of false advertising. In fact, this claim has about the same ring to it as the good old “You can afford that big McMansion” or “You can afford that suped-up brand new car.” A 5% initial withdrawal rate may seem nice in the beginning but reality will catch up eventually. The higher you set the initial withdrawal rate the more of a drop in your consumption pattern you might suffer if the market doesn’t cooperate.

### Conclusion

We actually have a lot more material and have to defer all of that to a future post. We’re already past 2,000 words and have only scratched the surface. We prepared another case study (the dreaded January 2000 retirement cohort), more comprehensive historical simulations (including the likelihood of a significant long-lasting drop in purchasing power for different CAPE regimes), and like to show several other smaller flaws in the GK methodology. Probably next week!

To wrap up today’s post, the initial question was: Is the Guyton-Klinger method overrated? False advertising sounds more appropriate. The GK-type rules seem to imply that they can offer higher initial withdrawal rates and better long-term success rates. True, but all that comes at the cost of potentially **massive** reductions in withdrawals (50%+ below the initial).

Oh well, what did we all expect? The GK-rules can’t square the circle by offering higher withdrawal rates and lower failure rates. If we wanted to be sarcastic we’d point out that GK won’t cure athletes foot either. If you want to use GK yourself make sure you’re aware of the downside (literally!), i.e., be prepared to curb consumption by 50% if things don’t work out. And that’s not just for a year or two, but potentially for a **decade** or more! That may be doable if your initial withdrawal is $80k or $100k and there is enough downside cushion. But for the folks with a tighter budget, GK would imply a significant probability of heading back to work during early retirement!

### 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** - Part 14:
**Sequence of Return Risk** - Part 15: More Thoughts on
**Sequence of Return Risk** - Part 16: Early Retirement in a
**low return environment**(The Bogle scenario!) - Part 17: Why we should call the 4% Rule the
**“4% Rule of Thumb”** - Part 18:
**Flexibility**and the Mechanics of**CAPE-Based Rules** - Part 19:
**Equity Glidepaths**in Retirement - Part 20: More thoughts on
**Equity Glidepaths** - Part 21:
**Mortgages**and Early Retirement don’t mix! - Part 22: Can the
**“Simple Math”**make retirement more difficult? - Part 23:
**Flexibility**and**Side Hustles!** - Part 24:
**Flexibility Myths**vs. Reality - Part 25: More
**Flexibility Myths** - Part 26: Ten things the “Makers” of the 4% Rule don’t want you to know
- Part 27: Why is
**Retirement Harder**than Saving for Retirement? - Part 28: An
**updated Google Sheet**DIY Withdrawal Rate Toolbox - Part 29: The
**Yield Illusion:**How Can a High-Dividend Portfolio Exacerbate Sequence Risk? - The Yield Illusion Follow-Up (SWR Series Part 30)
- The Yield Illusion (or Delusion?): Another Follow-Up! (SWR Series Part 31)

Very much appreciate your posts! I do have a question on your statement about the withdrawal amount in this post from the following excerpt:

“But the 0.2 guardrail is on top of the drop in the portfolio. If the portfolio is down by 50% and you hit the lower guardrail, the drop in the withdrawal is (1-0.5)x(1-0.2)=0.4 = 60% under the initial withdrawal.”

Here, you state that in this example if a portfolio drops 50% and hits the lower guard rail, the subsequent withdrawal amount is adjusted by BOTH the new portfolio amount multiplied by (1-0.5), and also the reduction in spending percentage (1-0.2). But it seems that per the reference you provided (http://finalytiq.co.uk/guyton-klinger-sustainable-withdrawal-rules/), it is hard for me to reconcile this with the following statement from that reference:

“The capital preservation rule: If the current withdrawal rate rises above 20% of the initial rate, then current spending is reduced by 10%.”

Here, it only refers to “current spending”, which one could reasonably interpret as the current spending level, which suggests the reduction relative to this level would not depend on reducing the -withdrawal rate- by an amount proportional to the product of the portfolio drop and the reduction in spending percentage together. Indeed, in the original paper, the capital preservation rule mentions only a drop in the current spending level to be acceptable for the current time period, and then uses this same spending level relative to the new portfolio value at the end of the current time period/beginning of the next time period to determine the appropriate course of action at the beginning of the next time period.

To this effect, when the time period length shrinks to zero, it seems that your claim is wholly true, as the effects of the capital preservation rule for small time period intervals rapidly converge on a basis for subsequent withdrawals that weighs both the portfolio value drop and the percentage drop equally. However, for period intervals that are larger (e.g. a year), it would take considerable time to converge on this result as it would require a sustained drop to last a long number of periods.

I bring this up because in cfiresim, the Guyton-Klinger method is modeled to update on a yearly time period, and as such it does not show the drastic drop in withdrawals that you show, even for 1966. I think that is because you are using a monthly drop, which with 12 samples in a year allows your result to converge faster on a much lower withdrawal value compared to the 1 sample a year in cfiresim. I believe this is because the larger time periods act as a filter, which result in withdrawal rates that seem substantially higher. To be fair, it is clear these higher rates would reduce the probability of success compared to the fine-grain higher-sample configuration of smaller time periods, and I have not quantified that–perhaps that would be a good place to investigate for further research.

The market never drops by 50% over-night. GK very much allows for a drop of 10% in several consecutive years. In the 1965 cohort you’d observe 6 (!!!) consecutive 10% drops., hence the drop in consumption from $40k to $21k. So you do see a significant drop in consumption.

The difference between my GK rule and the one in cfiresim is that, indeed, I do the calculations monthly, so the drop can happen faster. Also, I use the inflation-adjustments rule, as described in the original GK paper: Do no CPI-adjustment if the portfolio return in the previous period was negative. Thus, you see already some decline in real withdrawals before ever hitting the guardrail.

But also notice that in my calculations you recover much faster than in the cfiresim calculations. In my chart, the GK4% line is almost back to $40k again after 30y. While in the cfiresim numbers you’re still at $23k even after 30 years.

https://i0.wp.com/earlyretirementnow.com/wp-content/uploads/2017/02/swr-part9-chart3.png?resize=863%2C549&ssl=1

So, pick your poison: Do you want the GK rule to look like my chart (deeper drop, faster recovery) or in cfiresim (drop not quite a deep, but almost no recovery after 30y)?

So, the monthly frequency doesn’t make the GK rule look worse. It makes it look worse in one dimension, but makes it look better than cfiresim in another dimension.

Hope this explains the differences.