No changes this month. The portfolio is still below its risk target on a trailing one-year basis. Again, in an ideal world I’d be leveraging it to 12% annualized volatility. But given the limited toolbox at my disposal, remaining fully invested will have to suffice.
You can geek out with some data here. Below is actual performance data for the strategy since I implemented it.
This post marks the second rebalance check for my leveraged permanent portfolio. Based on some feedback from Twitter, I am making a small tweak to the volatility targeting overlay, and increasing the lookback period from 1 month to 1 year. The intention here is to make the portfolio less sensitive to sharp, short drawdowns in the underlying assets. The purpose of the volatility and trend overlays is not to avoid these types of drawdowns, but rather to adapt to regime changes.
Here is the current portfolio:
On a 1-year lookback this gives us a 9.2% return and 10.33% volatility.* Below the 12% target for the portfolio, despite being fully invested. You can nerd out on the lookback data versus a global 60/40 portfolio and SPY here. In an ideal world, if I had access to the full investment toolbox, I would actually leverage the portfolio to reach the risk target. But, as a small investor, being fully invested will have to suffice.
So, no changes this month.
Below is net performance since inception versus the S&P 500 (my actual allocation differs from target slightly due to transactional frictions, but not in a material way). Again, I wouldn’t normally expect the portfolio to perform this well against a 100% equity allocation over any arbitrary time period. But I think this time period offers an excellent out of sample test of the strategy’s efficacy and in particular its ability to tamp down risk.
* Fun Fact: 10.33% volatility for the portfolio in spite of the fact that individually, each asset in the allocation had a volatility above 12%. This is the magic of true diversification.
My latest note for Epsilon Theory is about possible futures. And Dune.
One of the recurring images in the book is what we in finance know as a probability tree. In the world of Dune, if you are at least a little bit psychic, and you amplify that psychic ability with a generous helping of hallucinogenic “spice,” you can catch a glimpse of the branching probability tree that is the as-yet-unrealized future.
Here in the investment and financial advice businesses, we, too, seem to have reached an evolutionary crossroads. I don’t claim to know exactly what the industry will look like in ten or twenty years. But like Dune‘s protagonist, Paul Atreides, I think I can peer through the haze of a spice trance to glimpse some of the branching possibilities.
I got a lot of great feedback on this note. In reflecting on it, there are a couple points I wish I’d articulated or emphasized more explicitly.
Non-Linearity In Causal Relationships
The imagery of a probability tree used in the note is oversimplified. In reality, discrete paths do not lead inevitably to particular futures with such-and-such probabilities. Rather, events exert a kind of gravity on one another. (See: The Three Body Problem) For example, if MMT were to become the fiscal policy paradigm adopted by our fiscal policymakers, it wouldn’t “automatically” mean that X, Y, and Z would follow as consequences. Rather, the “gravity” of this event would shift the positioning of events in probability space.
A “better,” but more conceptually challenging way of thinking about this is in terms of the light cone used in special and general relativity.
A detailed exploration of the light cone concept is beyond the scope of this post (A Brief History of Time by Stephen Hawking offers a good, in-depth introduction if this topic piques your interest). For simplicity’s sake I’ll rip the relevant principles regarding causality straight from the light cone Wiki:
Because signals and other causal influences cannot travel faster than light (see special relativity), the light cone plays an essential role in defining the concept of causality: for a given event E, the set of events that lie on or inside the past light cone of E would also be the set of all events that could send a signal that would have time to reach E and influence it in some way. For example, at a time ten years before E, if we consider the set of all events in the past light cone of E which occur at that time, the result would be a sphere (2D: disk) with a radius of ten light-years centered on the position where E will occur. So, any point on or inside the sphere could send a signal moving at the speed of light or slower that would have time to influence the event E, while points outside the sphere at that moment would not be able to have any causal influence on E. Likewise, the set of events that lie on or inside the future light cone of E would also be the set of events that could receive a signal sent out from the position and time of E, so the future light cone contains all the events that could potentially be causally influenced by E. Events which lie neither in the past or future light cone of E cannot influence or be influenced by E in relativity.
As events “fire” in space-time, they dynamically shape the geometry of possible futures. Of course, when we think about this in the context of politics, geopolitics, or economics, it is important to acknowledge that events/signals “fire” with different levels of intensity–they create proportionally greater or lesser perturbations in probability space.
If someone were to shoot me dead tomorrow it would not even cause a ripple in global probability space. The event would really only impact probability space in a way that is localized to me and my immediate personal connections. Maybe my local community.
If the President of the United States were to be shot dead, however, the event would “shock” global probability space. A much wider range of possible futures are impacted, distorted, and/or brought into play, and on a much larger scale.
The concept of “blowback” is interesting to consider in this context. The term is used in the intelligence community to describe unintended consequences resulting from covert ops. For example, you arm and train some Islamic fundamentalist religious groups to fight communism during the Cold War. Decades later, the same fundamentalists are using their arms and training to commit terrorist attacks against you. Blowback results from our inability to precisely forecast changes in the geometry of probability space.
We are not Paul Atreides.
And for what it’s worth, if you’ve read Dune: Messiah, you know that even Paul’s prescient vision lets him down in the end.
Some Thoughts On Permabearishness
On a completely unrelated note, I think it’s worth making a few comments on bearishness and permabearishness in particular. If you are not familiar with the term “permabear,” it refers to someone who is constantly calling for the end of the world and therefore refuses to put capital at risk in the equity markets, or is chronically short equities. Sometimes people mistake me for a permabear because I spend a lot of time thinking and writing about economic and investment risks.
There is an important difference between spending a lot of time and energy thinking about risk and refusing to put capital at risk, or being chronically short equities.
Why do permabears exist? Some are cynical charlatans who are permabears because they make a living as permabears. Other permabears get one bearish call right and it leads them down a path of perpetual bearishness as a result of overconfidence in their own prescient vision (there is a Dune reference for everything).
In my view, the core failing of permabears is confirmation bias. They become so myopically focused on justifying their perpetually bearish stance that they lose sight of the fact that you don’t actually make much money (any money?) as a permabear.
The core tenets of my personal investment philosophy these days are the following:
Minimax Regret > Utility Maximization
My affinity for barbell-type portfolios is the result. Rather than create “muddy” blends of fixed income and equity, strive for a convex risk/return profile. Use some method (simple annuity, permanent portfolio, T-bills) to create a kind of “floor” for a portfolio. Then take the remainder of your capital and place your high risk, high reward, high convexity bets. The goal is to create and maintain an asymmetrical risk/reward profile. Skewed to the upside, obviously.
It is not okay to be a permabear. In fact it is dumb to be a permabear.
It is okay to be a nervous bull.
It is okay to view the world through the lens of minimax regret instead of utility maximization (though you must acknowledge potential opportunity costs).
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.
Here is a question I get all the time, either directly or from colleagues on behalf of clients:
“I bought [INSERT RANDOM STOCK]. It is down 50%. Should I sell it or hold it?”
The answer should be obvious and it is that you sell ASAP. I kind of hate to say it (okay, not really), but if you need to ask someone like me this question, you had no business buying the damn thing in the first place. Note that the fact the stock is down 50% is basically irrelevant here. You should not take a position in a security if you have no framework in place for updating your views on the basis of new information.
There are lots of different ways to play the game. You can immerse yourself in 10-Ks and 10-Qs and try to find great businesses selling at reasonable prices. You can take the view that “price is all that matters” and trend follow or whatever. There are all kinds of sensible strategies for making money.
Hope is not one of them.
If you own something that has halved and the only reason you have for holding it is “gee, I really hate the idea of locking in a loss” then you are in trouble. No one will be able to begin helping you until you first help yourself by exiting the position. Hopefully it is at least in a taxable account and you can write it off. You can think of the slightly milder after-tax loss as discounted tuition.
When people talk about “dumb money” or “the suckers at the poker table” they are talking about hope-based investing.
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.
A colleague asked me for my thoughts on this piece by Bob Rodriguez. It is your usual anti-Fed, value investor screed. For example, he writes:
As for optimism, I would have some if I could see the insanity of the present monetary and fiscal policy environments changing for the better. But that seems like a very long, long shot. In the past two years, I’ve grown far more pessimistic, given what I see unfolding.
I have liquidated virtually 100% of my equity holdings and this occurred back in 2016 and 2017. I’ve always been early. I’ve deployed capital into 2-3 year Treasury bonds since I do not want to have any credit risk exposure in this distorted economic environment. As for risk assets, I’ve been acquiring rare, fully paid-for hard assets. I expect the latter to probably get hit in the coming recession but then they may well perform better in the ensuing monetary inflation. At least I don’t have to worry about managements leveraging their respective company balance sheets by buying back stock at elevated prices because the math works with these ultralow interest rates.
I am deeply sympathetic to a lot of this stuff on an intellectual level, but considerably more wary on a practical level. Below are my comments, edited slightly from my original email for clarity…
At a high level I basically agree with all of this.
The portfolio changes he describes are too extreme, in my view. I do think the US market has conditioned us to be overly complacent about equity risk over the last 30 years. But there is a huge potential opportunity cost to sitting in cash. I think there are much better ways to manage the kinds of risks we face in this environment such as vol targeting and/or trend following overlays. The problem with the permabearish approach he describes is that there is nothing to help him get back into the market if he ends up being wrong. He will just sit in cash tilting at windmills forever with the permabear crowd.
Regarding negative rates, the idea of owning negative yielding debt is not necessarily irrational if you believe rates will get more negative. The reason is that there is a non-linear relationship between price and yields (see below). For some reason we are taught all about duration in basic bond math but not convexity (convexity is the curvature). The greater the change in yields and the longer the duration of your bond, the more convexity comes into play.
Even with a negative coupon, you can potentially earn a positive return DEPENDING ON THE FUTURE PATH OF INTEREST RATES. If your view is that nominal yields are headed for -3%, -4%, -5% then it is perfectly rational to own negative yielding debt as from a price perspective you are potentially looking at equity-like returns. Who said fixed income had to be boring?
On top of that, it is possible for the owner of, say, negative yielding German Bunds to earn a positive yield by owning the bonds long and then swapping back to dollars using a currency swap or currency forwards.
That’s not to say I think negative nominal rates will achieve the policy objectives central banks have set out to achieve. In fact, I believe it’s just the opposite and the post-GFC and Japanese experiences provide empirical evidence in support of that view. But I do quibble with comments about owning negative yielding debt being “completely irrational” as I think folks making this argument are either forgetting their basic bond math or are ignorant of it. There is an important difference between merely being wrong about the future path of interest rates and being completely irrational.
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.
I’m a “probably stocks for the long run, most of the time” guy.
See, I’m pretty confident that in order to get rich, you’ve got to own equities. You probably also have to own equities to stay rich (to support drawing cash from a portfolio while preserving purchasing power).
Usually when people say “stocks for the long run” what they really mean is “US stocks for the long run.” And usually what they’ve done to arrive at this conclusion is extrapolate past returns from the US stock market since about 1926 or so.
When we do this with fund managers and stocks it’s performance chasing.
When we do it with asset classes and countries it’s asset allocation.
Particularly since we know major economies and empires have all mean-reverted historically. (There are literally no exceptions I can think of)
Now, I’m certainly not going to argue a bet on US stocks is a bad bet over the next 20 to 30 years. Especially considering the alternatives. In the grand scheme of things, if you’re going to make a massive directional bet, this is probably one of the better ones you can make. But there sure are a lot of assumptions embedded in that kind of allocation.
The ur-assumption is, of course, that asset allocation is an exercise in decision making under risk, like placing bets in casino games where the odds and payoffs are both known and fixed.
Asset allocation is an exercise in decision making under uncertainty.
A metaphor we often use to teach basic probability is that of picking colored balls from a bag. If you know there’s one red ball and nine green balls in the bag and the proportion remains static over time, you’ll always have a 10% chance of pulling a red ball.* This is the world as modeled by modern portfolio theory and mean-variance optimization.
Financial markets work more like this: every time you pull a ball from the bag, you have to turn your back, and the person holding the bag may or may not place another ball, either red OR green, into the bag. You can continue to assume a 10% chance of pulling a red ball, but the true distribution may turn out to be dramatically different over time.**
Most of what we think we know about asset allocation is a noble lie. We treat asset allocation as an exercise in decision making under risk because doing so makes it more amenable to neat and tidy mathematical models (not to mention neat and tidy sales pitches). In reality, we have no idea what the “true” distribution of returns looks like.
In fact, it’s extremely unlikely a “true” distribution of returns exists. Even if it did, it probably wouldn’t remain static. Why would it, given that we know economies and markets are complex, chaotic systems that are constantly changing? It should hardly come as a surprise that fancy statistical models based on decision making under risk repeatedly fail in the wild (see: Long-Term Capital Management; The Gaussian Copula).
As I’ve grown increasingly fond of saying: there’s no there there.
The single biggest change in my personal investment philosophy over time has been shifting from a utility maximization mindset to a regret minimization mindset. To me there are two key components to regret minimization:
(1) Get balanced beta exposure cheaply and efficiently. A little leverage is okay to help balance it all out. Emphasize robustness over maximization.
This is why over time I’ve become increasingly convinced strategies such as risk parity or leveraged permanent portfolio should be core building blocks for folks who want truly diversified portfolios. Grind out 5% real or so in the core. Make your high risk/high reward bets in a dedicated alpha sleeve.
However, I’d be remiss to conclude without noting that regret functions don’t generalize well. Your regret function is probably different from mine. In fact, it’s entirely possible your maximum regret is not maximizing utility (“leaving returns on the table”).
In that case, by all means, go ahead and maximize utility! But it’s still worthwhile to be explicit about the assumptions embedded in what you are doing.
* If we assign a value of 1 to “pick a red ball” and 0 to “pick a green ball” we can compute an “expected return” and standard deviation (“volatility”) for “pick a red ball.” Those values are 10% and 30%, respectively. Assuming T-bills yield 2%, “pick a red ball” has a Sharpe ratio of about .27. Somewhat amusingly, this is not too far off the long-run average Sharpe for the S&P 500.
** You should therefore be updating your views of the distribution over time. And it behooves you to assign low confidence levels to your views. A detailed examination of the math behind this is beyond the scope of this post but you can read an excellent discussion of the issue here.