Two weeks ago, I was orphaned by the weekend reading list from BankerOnWheels. But thankfully, Monevator’s list threw me a lifeline: two posts, both interesting, and together they fill in the blanks.
Here’s why they hit differently: they almost complete each other.
First up, White Coat Investor drops a spicy take. It’s a deep dive into “Sequence of Return Risk,” but Dahle argues we should call it “Sequence of REAL Return Risk.” And he’s right. 1966 is the textbook starting year for the “worst time to retire” (according to US markets). But if you actually look at S&P 500 returns from that era, it wasn’t some Black Monday doom scenario: the 10-year annualized return from 1966–1975 was around 3.3%. Not great, not terrible, with some good years mixed in.
So what actually crushed retirees? The 1970s inflation monster. Annual inflation was in beast mode for more than a decade, spiking into double digits like it was chasing a high score.
And here’s where the 4% rule comes into play: retirees bump up their annual withdrawals by inflation, not by portfolio performance. So when inflation’s running hot, you’re forced to take out more cash each year, no matter if the market’s stuck in the mud. Crucially, unlike bad markets, purchasing power doesn’t mean revert. It doesn’t snap back. For that, we would need multi-year DEFLATION. Once purchasing power disappears, it’s gone for good, like a lost sock…except it’s your future lifestyle.
This story is important because the “4% rule” is presented as such. And all the financial asset returns that you read around are also presented on a nominal basis. Inflation kills financial plans because is loud until it isn’t:
After the 2022 shock, everyone was ready to pop champagne: “inflation is defeated!” But not so fast. In the US and UK, it’s still lurking, running noticeably above target. If you’re looking at 2.9% and thinking, “Eh, that’s not much different from 2%,” you might want to hit refresh on your memory of compounding. That lesson’s worth a re-read.
We all need mental shortcuts, helps us keep our heads from exploding with information overload. That 2% inflation we got used to? It was a nice, tidy convenience. Too bad we don’t live in that world anymore. Time to update your mental model. Continental Europe isn’t going full UK-mode on inflation just yet, but the impact and fallout from a 2022-level shock are still very much in play.
Which brings us to the second post in the Monevator lineup: “The Best Strategies for Boosting Starting Withdrawal Rates in Retirement” from Morningstar.
Morningstar’s crew serves up five strategies to spend more than the classic 4% rule:
Look at those first two strategies: 5.7% instead of 4%, ain’t that a miracle?
Well, the issue is THAT 5.7% doesn’t revalue with inflation each passing year. The authors flag this point, but I don’t think they give it enough weight. Most readers are going to walk away thinking, “Wow, 5.7%. That blows 4% out of the water!” and totally gloss over how quickly inflation can chew through those numbers. Inflation’s not just background noise: it hits your bottom line fast, and if you forget that, the headline rate starts looking a lot more generous than it really is.
This table just makes the issue worse, because the REAL cash flow volatility for those 5.7% strategies is even higher. And remember, we’re talking about a 40% stocks / 60% bonds portfolio, not the greatest mix when inflation is high.
That said, I’m pretty bullish on the Probability-based guardrails method.
The fact that median ending wealth is so low? That’s not a bug, it’s by design (even if Morningstar doesn’t quite spell it out). If the whole point is to maximize spending, then obviously, ending wealth should trend lower. That’s the trade-off.
Here’s the beauty: you can tweak this approach for longer time horizons. Say you’re 40 and want to retire early: you set your horizon at your life expectancy and then re-run the numbers every year, adjusting based on your actual stats. Dig a little deeper, and the framework lets you start with a conservative probability of success, like 80%, and only dial back your spending when that probability really drops, some researchers say even as low as 25% (at 50%, that’s just part of the normal path; no need to panic or cut spending, since that dip was baked into the original plan).
Of course, there’s a trade-off: the longer you hold out before cutting, the harder that cut might need to be. The upside? You’ve got the flexibility to move the dial for your own comfort level. The framework can easily quantify those portfolio gaps: “If your portfolio falls below EUR 1.5m, you’ll need to cut your spending by 10k a year.” That gives retirees peace of mind, they know exactly when and how much to adjust. And for the early-retired crowd, it’s a lifeline: you could snag a side gig for 10k/year to fill the gap, and wait for returns to turn around.
Here is the obvious (?) issue with this type of strategy, applied to our preferred naïve portfolio (and its seemingly impressive stats):
A nice sample of one 🙂
How much would you trust those numbers given the following inflation-adjusted rolling CAGR?
To be clear, even a 100% stock portfolio isn’t immune to a garbage-in, garbage-out Monte Carlo simulation. If you feed bad assumptions in, you get junk results out, no matter how aggressive your asset mix. Here, we’ve got more moving parts, more ingredients in the stew, which means more ways to get fooled by slowly vanishing excess returns.
DBMF, even in its SIM version, has less data than KMLM but it seems to smooth further the results:
Adding more strategies to handle model risk, especially for those “alternative sleeves” in the portfolio, should boost robustness. In theory, that ups our confidence in future withdrawal rates.
To borrow from David Dredge, the classic SWR was like picking the lowest average speed that keeps your car from flying off the track, no matter what curve gets thrown at you. Dynamic retirement strategies? They’re about adjusting the speed on the fly, slowing down withdrawals when your portfolio veers off-road.
But here’s the catch: our strategy actually has brakes built in. So now the real question is, “Will those brakes actually work when you need them most?” Less leverage isn’t the magical answer. If the brakes fail, you’re going into that wall, just faster if you had the pedal down harder, but either way, you’re still hitting it. Honestly, I’m not sure there’s a slam-dunk answer, other than installing multiple, independent brake systems. That way, if one fails, you’ve got another ready to kick in.
What’s actually comforting here is that this strategy has done its job in what I’d call true out-of-sample scenarios: real-world, not just backtest theatre (the TILS Model Portfolio is 4 years old). Especially when you stack it up against what Morningstar tested, it’s held up.
Maybe you’re thinking I cherry-picked the assets, “What about all those trend-following funds that blew up in the past? Isn’t this just riding gold’s bull run?”
But you should also consider that the so-called naïve portfolio has a small stocks allocation, and the sample includes plenty of stretches where bonds, trend, or gold did absolutely nothing for you. Miserable returns, all around. The crucial detail? By design, they didn’t all tank at the same time. That’s what gives the portfolio its staying power.
What I am reading now:
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