Welcome to another part of my Safe Withdrawal Rate Series. Today’s topic: Bucket Strategies in retirement. As you know, my blogging buddy Fritz Gilbert has written extensively on this topic at his Retirement Manifesto blog, for example:
Fritz’s most recent post on the Bucket Strategy started a lively back-and-forth on Twitter, and it seemed appropriate to pursue a more detailed discussion with more than 280 characters per answer in a “fight of the titans” blog post. So if you haven’t done so already, please check out our awesome discussion over on Fritz’s blog:
The response was overwhelmingly positive, and we decided to craft a follow-up post here on my blog. We came up with two new questions, and we also need to address two major themes from the comments section in Part 1, specifically, the role of simplicity and behavioral biases in retirement planning.
Welcome to a new installment of the Safe Withdrawal Rate Series, dealing with Bucket Strategies. This is one approach that’s often considered a viable solution to the dreaded Sequence Risk Problem. Simply keep buckets of assets with different risk characteristics designated to cover expenses during different time windows of your retirement. Specifically, keep one or more buckets with low-risk assets to hedge the first few years of retirement. And – poof – Sequence Risk evaporates, just like that! Sounds too good to be true, right? And it likely is. Long story short, while there are certain parts of the bucket strategy that can indeed partially alleviate the risk of retirement bust, bucket strategies are by no means a solution to Sequence Risk. Let’s take a look at the details…
What? A new case study? I know, I had promised myself to wind down the Case Study Series I ran in 2017/18 after “only” 10 installments. It was a lot of work and a lot of back and forth via email. It takes forever! I mean F-O-R-E-V-E-R! But then again, there’s always a reason to make an exception to the rule! Jonathan and Brad from the ChooseFI Podcast had a very interesting guest on their show this week (episode 152). Becky talked about her experience of a late start in getting her and her husband’s finances in order. They started at around age 50 and became Financially Independent (FI) in their early 60s and retired a year ago. I should also mention that Becky recently started her own blog, appropriately labeled Started At 50, writing about her path to FI and RE so make sure you check that out, too.
In any case, Jonathan and Brad asked me to look at Becky’s numbers because I must be some sort of an expert on Safe Withdrawal Strategies in the FIRE community. I chatted with Jonathan and Brad about my case study results the other day and this conversation should come out as this week’s Friday Roundup episode. Because there’s only so much time we had on the podcast and I didn’t get to talk about everything I had prepared, I thought I should write up my notes and share them here. Heck, with all of that effort already spent, I might as well make a blog post out of it, right? That’s what we have on the menu for today… Continue reading “A Safe Withdrawal Rate Case Study for Becky and Stephen”→
We made it through October, without much volatility this time – what a change compared to last year! We even got to a new all-time high in the S&P 500 in the last few days. When you reach new records, the pessimists come out of the woodworks and declare that “this is the top” and the next bear market must be right around the corner! It’s like clockwork! And if you go to the popular forums and Facebook Groups in the FIRE community, you’ll see people poking fun at the perma-pessimists. Quite appropriately, I think!
But why are people still a bit nervous about corrections and bear markets and market crashes? Being retired now, I have to admit I feel at least a little bit uneasy right now. Why’s that? If I wanted to quantify how afraid I am of something I’d do so as follows: Fear depends on both the probability and the magnitude of something scary happening: FEAR = The probability of something scary TIMES the magnitude of something scary
In my recent post My thoughts on the “Upcoming Recession” I wrote about the probability part. I personally don’t think that the economy is at the brink of a major slowdown (yet) and with the economic growth trend, still intact the stock market will likely chug along. This all looks like a mid-cycle, temporary soft spot.
What makes me nervous about the bear market prospect, though, is the magnitude part; the fact that IF a bear market were to occur (however unlikely that may be) we’d most definitely go through some anxiety for a while. That’s true for all retirees and even folks close to retirement. Probably not so much for everybody just starting out in their accumulation phase, see the post “How can a drop in the stock market possibly be good for investors?” from earlier this year.
Quite intriguingly, though, if you look around in the FIRE community I get the sense that people minimize how scary a bear market will be if it were to start today. And the thought process is:
The market will always recover (see the chart above)
Most bear markets last only about one to two years
It sounds like the bear has really lost its teeth! So, why am I not convinced? There are multiple problems with that line of thinking. That 1-2 years estimate wildly underestimates how long it takes to recover from a bear market. If you do the math right a bear market will appear a lot scarier than it’s commonly portrayed. Let’s see why…
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.