Over the last couple days I’ve had the pleasure of corresponding with David Merkel of The Aleph Blog over differences in our S&P 500 expected returns modes. (Mine was much higher than his). Upon comparing models, I discovered I’d made a huge mistake. I’d essentially included only corporate debt in my calculation, excluding a huge swath of government liabilities from the total figure.
After adjusting my numbers to correct for this, and updating the model, I get a 3.74% expected return for the next 10 years. This is consistent with David’s 3.61% estimate. The small difference that remains is likely down to some minor differences in the time periods we used to estimate our models, as well as the type of S&P 500 return we use in the calculation (I believe David uses the price return and then adjusts for dividends, whereas I simply regress the S&P 500 total return against the “allocation” data).
Previously I’d been referring to my results as “A World of Meh.” I think I’m now comfortable revising that down to “A World of Bleh.” (“Meh” is kind of like an indifferent shrug, while with a “bleh” you are maybe throwing up in your mouth a bit)
I’ll give David the last word here, since I think his take on all this is a nice summary of the quandary investors face these days:
Not knowing what inflation or deflation will be like, it would be difficult to tell whether the bond or stock would be riskier, even if I expected 3.39% from each on average. Given the large debts of our world, I lean to deflation, favoring the bond in this case.
Still, it’s a tough call because with forecast returns being so low, many entities will perversely go for the stocks because it gives them some chance of hitting their overly high return targets. If this is the case, there could be some more room to run for now, but with nasty falls after that. The stock market is a weighing machine ultimately, and it is impossible to change the total returns of the economy. Even if an entity takes more risk, the economy as a whole’s risk profile doesn’t change in the long run.
In the short run it can be different if strongly capitalized entities are taking less risk and and weakly capitalized entities are taking more risk — that’s usually bearish. Vice-versa is usually bullish.
Anyway, give this some thought. Maybe things have to be crazier to put in the top. At least in this situation, bonds and stocks are telling the same story, unlike 1987 or 2000, where bonds were more attractive. Now, alternatives are few.
2Q19 Fed Z1 data is out so I have updated my little S&P 500 expected returns model. The model and its origins have been discussed rather extensively here on the blog so I am not going to belabor its strengths and weaknesses going forward. From a long-term forward return perspective, the message remains: “meh.” As of June 30 it was predicting a 7.81% annualized return for the next decade.
It is interesting to note that the model disagrees with the dire prognostications of much of the investment world regarding forward-looking S&P 500 returns. Many shops out there are predicting low single digit or even negative returns over the next 7-10 years. These folks correctly called the tech bubble in the late 1990s but missed the post-crisis rebound. The model, meanwhile, caught both.
Given the output from the model, and the investment opportunity set more broadly, I’d bet with the model when setting expectations for the next 10 years.
What I think those shops are missing, and what the model captures, is the TINA Effect.
For many investors There Is No Alternative to owning equities.
Given that global interest rates remain very low, investors need to maintain high levels of equity exposure to hit their return hurdles. In the US, for whatever reason, the aggregate equity allocation typically bounces around in the 30% – 40% range. Unless something occurs to dramatically and permanently shift that range lower, I suspect forward returns will end up being a bit better than many people are predicting these days.
Today marks the second rebalance of my leveraged permanent portfolio with its volatility (12% target) and trend following overlays. I thought it might be fun to do brief posts on the monthly rebalances going forward, partly to keep myself honest and partly to record for posterity what it “feels like” to be invested this way.
Perhaps unsurprisingly, the portfolio is now below its risk target, with a trailing volatility of only 5.16%. So the cash position created at the last rebalance will now go back to work in ex-US equity (most segments of ex-US equity appear to have poked back above their 200-day moving averages). In fact, I need to be fully invested now and will STILL be below my risk target.
The new target portfolio looks like this:
When the leverage employed within NTSX is taken into account, you end up with:
28% S&P 500
19% Laddered Treasuries
4% EM Large Cap
4% EAFE Small Cap
14% EM Small Cap
15% EAFE Large Cap
~116% notional exposure, just shy of 1.2x leverage
Note that the weights of the portfolio as implemented will differ modestly from this “ideal” due to transactional frictions and such. For example, in an ideal world I would reallocate the two small Vanguard positions across the whole portfolio rather than overweight ex-US equity. However, I recently rolled this account over to a new platform and am trying to be mindful of transaction costs. And anyway, if you’ve read this blog for any length of time you’re no doubt familiar with my view that, in the grand scheme of things, these little overweights and underweights don’t materially impact portfolio performance.
Here is updated performance versus the S&P 500 for context as of pixel time:
Despite being so short, it’s an interesting period to look at live performance for the strategy (net of fees and transaction costs) as it exhibits precisely the type of behavior you would expect from backtests of both leveraged and unleveraged permanent portfolios. The portfolio protects well during periods of broad market stress but lags during sharp rallies. Additionally, it’s worth noting that gold has had an exceptional run during this brief period, which is a complete coincidence.
Of particular interest to me was the second piece, which examines a permanent portfolio with a trend following and volatility targeting overlay. As I’ve written before, I am hardwired as a mean reversion guy psychologically. So getting on board with trend following was (and remains) really hard for me. For no good reason other than my own biases, I might add. But I’ve gradually come around to the idea.
[T]he strategy has a beneficial propensity to self-correct. When it makes an incorrect call, the incorrectness of the call causes it to be on the wrong side of the total return trend. It’s then forced to get back on the right side of the total return trend, reversing the mistake. This propensity comes at a cost, but it’s beneficial in that prevents the strategy from languishing in error for extended periods of time. Other market timing approaches, such as approaches that try to time on valuation, do not exhibit the same built-in tendency. When they get calls wrong–for example, when they wrongly estimate the market’s correct valuation–nothing forces them to undo those calls. They get no feedback from the reality of their own performances. As a consequence, they have the potential to spend inordinately long periods of time–sometimes decades or longer–stuck out of the market, earning paltry returns.
The permanent portfolio concept works because it combines assets that are essentially uncorrelated across economic and market regimes (Treasury bonds, gold, equities). But within any given regime, assets can remain out of favor for extended periods of time.
Can a trend following and volatility targeting overlay help improve the return profile? I think the above linked blog posts provide compelling evidence that it can.
So I’d like to conduct a live experiment to test this out of sample. With my own money.
As I’ve mentioned before, the core of my portfolio* is now invested in a leveraged permanent portfolio:
There is nothing magical about either VMMSX or VINEX. These are just residual holdings in an old Roth IRA (we will revisit them in a bit down below). You may also recall that NTSX is allocated 90/60 S&P 500 and laddered Treasury bills. So the overall asset allocation looks like this:
29% S&P 500
19% Laddered Treasury Bonds
23% Emerging Markets
10% Ex-US SMID Cap Equity
(116% notional exposure a.k.a ~1.2x leverage)
The thing that keeps me up at night is the allocation to emerging markets and ex-US SMID cap equity. I am willing to place a bet on these market segments but I am also acutely aware that I could be wrong. Very wrong. For an extended period of time.
And this is where I think a trend and volatility management overlay can help. Rather than put my finger in the air to judge whether to double down or fold my hand, I’ll let feedback from market prices help me adjust the views expressed in my portfolio.
Here’s how it will work:
Step 1: First, check trailing volatility for the entire portfolio. If 12%, do nothing. If greater than 12%, proceed to Step 2. There’s nothing magical about 12%. I’m just trying to pick a high enough target so I’m biased toward remaining fully invested.
Step 2: Check trailing volatility for portfolio assets. For those with 12% or less, do nothing. For those with 12%+, proceed to Step 3.
Step 3: Check each asset’s price against its 200 day moving average. If above the 200 day moving average, do nothing. If below, trim positions to create cash such that overall trailing portfolio volatility falls falls to around 12% (transaction costs and taxes must be taken into account here).
Basically what we’re doing is volatility targeting by taking money from assets with poor price trends. If we were to find ourselves below target on overall volatility, we would check portfolio assets and add cash to the assets with higher volatility and strong price trends.
I ran an initial monthly rebalancing check on 8/21. Unsurprisingly, the portfolio was well above the 12% volatility target, at 17.27%. GLD, VMMSX and VINEX were all well above the 12% threshold. However, GLD is also trading well above its 200 day moving average. Thus, I trimmed significantly from VMMSX and VINEX to add cash and bring trailing volatility back to target. (In an ideal world we would actually risk-balance the portfolio as well, so that each asset held in the portfolio contributed the same amount of volatility. Unfortunately, at least as far as I am aware, I don’t have the tools available to do this in a small account)
You can compare the “before and after” portfolios here.
This is just a rebalancing mechanism for what is, on its own, a fairly well-balanced portfolio. Except here you are favoring the assets that are “working.” We are effectively mean-variance optimizing a highly diversified portfolio over short time horizons. Because we are optimizing more frequently, we are better positioned to adapt to regime changes than we are when using longer time periods.
Here are the results from my leveraged permanent portfolio since May. The timing is completely coincidental, and most definitely favors the permanent portfolio, but I think it’s compelling “live” evidence nonetheless (note that the first overlay-based rebalance did not take place until August 21).
*Ex-401(k). 401(k) investment options are literally the worst.
Today we’re going to talk about how a lot of what is passed off as diversification does not actually provide much in the way of diversification. To illustrate this we will look at two equity allocations. The first is “diversified.” It owns all kinds of stuff. REITs. Developed market international equities. EM equities. Even ex-US small caps. Wow!
The second portfolio, meanwhile, consists solely of vanilla US large cap equity exposure.
You might think the first allocation would show meaningful differentiation versus the second in terms of compound rate of return, as well as drawdown and volatility characteristics.
It’s because correlations across these assets are high.
As you might expect, correlations are especially high across the three US equity buckets. A full 65% of the portfolio is invested across these three market segments. Just because you have exposure to a bunch of different colored slices in a pie chart does not mean you have exposure to a bunch of differentiated sources of risk and return.
Now, I’m not Jack Bogle telling you to invest only in US large cap stocks. Limiting exposure to country and sector-specific geopolitical risks or asset bubbles (see the early 2000s above) is one good reason to own a global equity portfolio. However, I AM telling you if you want to meaningfully alter the risk and return characteristics of a portfolio, tweaking weights at the margins in this kind of allocation isn’t going to do it.
Perhaps you think manager selection will do it.
Maybe if you allocate to three or four managers and leave it at that; and the managers all perform to expectations (well enough overcome any expense drag); and because of that stellar performance you don’t make significant mistakes timing your hiring and firing decisions… maybe then manager selection will move the needle for you.
But most of us don’t build portfolios concentrated enough for it to matter all that much. And most of us pick a few duds here or there. And we are terrible at timing decisions to hire and fire managers.
Much of the time we spend hemming and hawing about the minutiae of asset allocation and manager selection is therefore wasted. Should emerging market equity be a 5% or 7.5% weight in the portfolio? I don’t know. More importantly, I don’t care. It’s a 250 bps difference in weight. Just do whatever makes you (or your client) feel better.
In fact, if you’re going to add EM at a 5% max weight because some mean-variance optimization shows it marginally improving portfolio efficiency, you officially have my permission to avoid it all together. The same goes for your 2.5% allocations to managed futures and gold.
I think there are four main reasons why this state of affairs persists:
Many folks, even professionals, don’t understand how the math works. Most people I’ve shown this kind of analysis are surprised how little difference there is in the above performance characteristics.
Many folks who do understand how the math works see the truth (rightfully) as a potential threat to their job security.
Advisors and allocators sometimes worry if they don’t futz and fiddle with things at the margins or throw in some bells and whistles, clients may question what they’re paying for. (My friend Rusty Guinn refers to this as adding Chili P to the portfolio)
At the same time, advisors and allocators can’t futz and fiddle so much they look too different from their peers and the most popular equity indexes, lest impatient clients fire them and abandon their otherwise sound financial plans during a temporary run of weak performance.
All these are valid concerns from business and self-preservation and behavioral finance perspectives. But they don’t change the math.
If your goal is to harvest an equity risk premium and play the averages as cheaply and tax efficiently as possible… then do that.
If you want to concentrate your bets in hopes of generating massive gains and you’re comfortable with the idiosyncratic risk that entails… then do that.
If you want to employ a barbell or core-satellite structure to balance cheap beta exposure with a selection of (hopefully) substantial, alpha generative bets… then do that.
Because if you waver, and you combine this and that and the other philosophy because you’re simultaneously afraid of looking too different and not differentiated enough… then you’re going to end up with something like the world’s most expensive index fund.
This is a quick post to share an update of this running model of expected S&P 500 returns using Federal Reserve data. As of March 31, the model predicted an 8.12% annualized return over the next 10 years. This has likely come down a bit further since then as the market rallied. As of today, we might be somewhere in the 6-7% range.
Given there’s so much wailing and gnashing of teeth over macro risks these days it’s worth emphasizing a couple points.
First, this model is useless as a short-term timing signal. Don’t try and use it that way. If you’re looking for short-term signals you need to be looking at trend following systems and such.
Where I think there’s some utility here is as a data point you can use to help set longer-term return expectations and guide strategic asset allocation decisions (particularly when used alongside other indicators like credit spreads). When the aggregate equity allocation is close to 40% or above, it signals lower expected returns and argues for taking down US equity risk. Between 30% and 40% it signals “meh.” Probably not worth making any adjustments in this range. At least not on the basis of this model. At or below 30%, however, the model argues for adding equity risk.
Also, what I like about this model is that unlike indicators such as the CAPE or market cap/GDP what you are really measuring here is the aggregate investor preference for fixed income versus equities. When investors are very comfortable owning equities they bid up prices and expected returns fall. When investors are not comfortable owning equities they sell, prices fall and expected returns rise.
That’s the ball game.
No macro forecasting is required.
You don’t have to make any judgment calls on valuations, either.
What I would love to do eventually is run this for countries outside the US. What I suspect is that the ex-US models would show similar efficacy but with different “preferred” bands of equity exposure based on the culture of equity ownership in each country and whether or not there’s a significant impact from “hot money” flows from foreign investors.
I’m not aware of a straightforward way to find the data needed to do this. But if anyone has suggestions, please drop me a line.
May afforded an interesting opportunity to test the leveraged permanent portfolio strategy out of sample. (For previous posts on the permanent portfolio, see here and here) Below is data showing the results for two different leveraged permanent portfolio implementations, compared to the Vanguard Balanced Index Fund (an investable proxy for a 60/40 portfolio) and SPY. You can do a deeper dive into the data here.
NTSX’s laddered Treasuries provided better downside protection than the StocksPLUS bond portfolio here. But the gold exposure was also a major help, with GLD returning +1.76%. Obviously this is just a single month of performance, but the results are consistent with what you might expect based on backtests of the strategy.
Notice that the performance pattern is similar during the 4Q18 drawdown. In each case, the drawdowns are less severe than even those experienced in the 60/40 portfolio due to the diversifying impact of the gold. Because again, where the leveraged permanent portfolio shines is downside protection. You aren’t capturing all the upside of a 100% SPY allocation, but you’re capturing only a fraction of the downside.
Since December 2004, the PSPAX/GLD portfolio has captured 60% of the upside of SPY but only 43% of the downside. The asymmetry means PSPAX/GLD slightly outperforms SPY over this time period, but with less volatility. More importantly, the max drawdown is only a little more than half as bad.
Still, in my view the biggest problem the leveraged permanent portfolio presents for investors is precisely that its outperformance comes in down markets. This isn’t a sexy way to make money. It’s not the kind of thing that impresses people at cocktail parties. The behavioral challenges this presents should not be underestimated.
But personally, I’ll take a 10.62% safe withdrawal rate over cocktail chatter any day.