Welcome back to our Safe Withdrawal Rate Series! Last week’s post on Sequence of Return Risk (SRR) got too long and I had to defer some more fun facts to this week’s post. Again, to set the stage, I can’t stress enough how important Sequence of Return Risk is for retirement savers. In fact, after doing all this research on safe withdrawal rates (start series here, and also check out our SSRN research paper) if someone asked me for the top three reasons a retirement withdrawal strategy fails I’d go with:
Sequence of Return Risk,
Sequence of Return Risk,
and let’s not forget that pesky Sequence of Return Risk!
Huh? Isn’t that lame? Surely, low average returns throughout retirement ought to be included in that list, right? Or even top that list, right? That’s what I thought, too. Until I looked at the data! Let’s get rolling and look at some more SRR fun facts.Read More »
This is a long overdue post considering how much we’ve written about safe withdrawal rates already. Sequence of Return Risk, sometimes also called Sequence Risk, is the scourge of early retirement. Or any retirement for that matter. So, here we go, finally, we have a designated post on this topic for our Safe Withdrawal Rate series (check here to go to the first post and also make sure you download Big Ern’s SSRN working paper on the topic).
Besides, in case you haven’t heard it, yours truly, Big Ern, was asked by Jonathan and Brad at ChooseFI to be an occasional contributor to their awesome Financial Independence podcast. Specifically, I’ll be the in-house expert on everything related to safe withdrawal rates. And that’s alongside an A-plus-rated team of experts: real estate guru Coach Carson, tax expert The Wealthy Accountant, and business guru Alan Donegan from PopUp Business School! How awesome is that? Because Sequence of Return Risk is something we’ll cover in the podcast soon as part of a crowdsourced case study, I thought it would be a good time to have a go-to reference post on the topic here on our blog. So, once again, make sure you head over to the ChooseFI podcast:
Welcome back to the Safe Withdrawal Rate series. 13 installments already! As requested by many readers, both in the comments section and via email, I wanted to look into one intriguing method, called “Prime Harvesting” (PH) to dynamically shift the stock vs. bond allocation during retirement. Where does this post fit into the big picture? Recall that parts 1-8 of our series dealt with fixed withdrawals and fixed asset allocation (same % stocks and bonds throughout retirement). Make sure you check out our SSRN working paper, now downloaded over 1,000 times!
Parts 9-11 dealt with how to adjust the withdrawal amounts while keeping the asset allocation fixed (Guyton-Klinger, VPW, CAPE-based rules, etc.). Prime Harvesting does something completely different: Keep the withdrawal amount constant, but use a dynamic stock/bond asset allocation to (hopefully) squeeze out some extra withdrawal wiggle room; the Northwest corner in the diagram below. Almost uncharted territory in our series!
Eventually, of course, we like to move to that Northeast corner: Dynamic withdrawals and Dynamic Asset Allocation. But let’s take it one step at a time! Let’s see what this Prime Harvesting is all about.
When we read about withdrawal strategies in early retirement, the cash cushion is often one crucial ingredient. Simply keep a little bit of cash sitting around on the sidelines, dig into that cash during an equity market drawdown and avoid selling equities until the next recovery. How much cash? Well, the Global Financial Crisis raged for “only” 18 months and the average garden-variety recession should last a year or even less. Thus, even if we assume that the equity market takes a little bit longer to recover it will take only very little cash and very little opportunity cost to achieve this. The whole issue of Sequence of Return Risk is solved! Who knew this was so easy? This is almost too good to be true! Well, unfortunately, it might be just that; too good to be true.
Here are our top six concerns about the cash cushion:Read More »
After a three week hiatus from our safe withdrawal rate research, welcome back to the next installment! If you liked our work so far make sure you head over to SSRN (Social Science Research Network) and download a pdf version. It’s a free 47-page (!) pdf working paper covering parts 1 through 8:
But 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. Add to that our two-month-long bashing of the static 4% rule and people may wonder:
What withdrawal rule do we like?
True, we proposed a lower initial withdrawal rate (3.25-3.50% depending on future Social Security income), but that’s just the starting point. We have written here and elsewhere that this withdrawal rate is not set in stone. How do we go about adjusting the withdrawals in the future? How did different dynamic withdrawal rules perform in the past? How do we even measure how much we like a withdrawal rate rule? Today, we like to take a step back and gather a list of criteria by which we like to evaluate different (dynamic) withdrawal rules. Then simulate a bunch of withdrawal rules and assign grades.Read More »
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. Read More »
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.Read More »
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. Read More »
One commenter the other day had a good suggestion: Publish the Excel spreadsheet that we use in our safe withdrawal rate research. Great idea! There is only one problem: we didn’t use Excel to calculate any of the SWRs. We did use Excel to create some tables, but the computation and most charts were all done using GNU Octave, a free number-crunching programming language, similar to Matlab.
But we still liked the idea of creating a tool to run some quick SWR calculations. In Octave, we can calculate a large number of simulations and calculate safe withdrawal rates over a wide range of parameter value assumptions. Millions and millions of SWRs over many different combinations of parameter values (retirement horizons, final asset value target, equity shares, other withdrawal assumptions). That would have been cumbersome, probably even impossible to implement in Excel. But a quick snapshot on how one single set of SWR parameters would have performed over time? That’s actually quite easy to do, even though there are 1,700+ different retirement cohorts between 1871 and 2015.
If you’ve been following our series on withdrawal rates (part 1 here) you have noticed that we’re quite skeptical about the 4% rule. That would be especially true for early retirees with a much longer horizon than the standard 30 years. Though, by reading through some of the research from the heavy hitters in the retirement research world, even the foundation of the 4% rule over 30 years seems to be crumbling a little bit:
Wade Pfau has been warning that due to high equity valuation and low bond yields the Trinity Study success rates are likely overrated. His argument is similar to ours in Part 3 of this series: we live in a low return world now and comparisons with past average returns could overstate the success probability of the 4% rule. He uses a slightly different methodology (Monte Carlo simulations) but reaches similar results.
Even Michael Kitces, arguably one of the great defenders of the 4% rule, has (inadvertently?) demonstrated that the 4% rule over 30 years isn’t all that sound. In the discussion after the famous “ratcheting post,” some readers (including yours truly) pointed out that we can’t replicate the success of the 4% rule with 1965/66 starting dates. Nothing to worry about, Kitces replied, all you needed to do is to use a very short-term bond (1-year T-bills) for the bond allocation, and you sail smoothly during the 1970s. Who would put 40% of the portfolio into 1-year Treasury bills (essentially CD interest rate) rather than trying to harvest the term premium of longer-term bonds? Very easy: someone with 20/20 perfect hindsight who knew that longer duration 10Y bonds will get hammered in the 70s and sink the 4% rule even over a 30-year horizon.
And I just became a little bit more skeptical about the 4% rule even over a 30-year horizon! But there is (at least) one prominent 4% SWR firewall still standing. In countless blog posts, discussions, forums etc. I have heard this quote (or variations of it):
“The 4% rule worked just fine during the Tech Bubble and Global Financial Crisis”