10/19 Permanent Portfolio Rebalance

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:

201910_pp_rebalance

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.

201910_pp_performance

* 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.

 

It’s Worse Than I Thought

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).

SP500190630ERREV
Source: Federal Reserve Z.1 Data / Demonetized calculations / Corrections from David Merkel

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 Expected Returns Update

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.

2Q19SP500ER
Source: Fed Z.1 Data; Demonetized Calculations

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.

Not great. But not dire, either.

Meh.

09/19 Permanent Portfolio Rebalance

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:

201909_PP_Rebalance

When the leverage employed within NTSX is taken into account, you end up with:

28% S&P 500

19% Laddered Treasuries

32% Gold

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:

201909_PP_Performance

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.

Permanent Portfolio + Trend

When I first wrote about the permanent portfolio, Adam Butler of ReSolve Asset Management (@GestaltU on Twitter) pointed me to a couple of pieces he’d done on the concept. They are both worth reading:

Permanent Portfolio Shakedown I

Permanent Portfolio Shakedown II

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.

The main reason is this: trend following ensures you incorporate market feedback into your investment process. As Jesse Livermore of Philosophical Economics writes in one of his exceptional pieces on trend following:

[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:

35% GLD

32% NTSX

23% VMMSX

10% VINEX

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:

35% Gold

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).

1908PPerf
Source: Morningstar

*Ex-401(k). 401(k) investment options are literally the worst.

Stocks For The Long Run?

I’m not a “stocks for the long run” guy.

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).

BUT

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.

We like to pretend this is a disciplined asset allocation process when really it’s just a massive directional bet on the US equity market. A massive directional bet based on a relatively limited historical data sample. (btw , your “diversified” RIA and wirehouse models typically make this same bet but with a dash of Chili P for flavor)

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.

Classic.

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.

It isn’t.

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.

(2) When you do take shots at alpha generation, make them count.

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.

Smoke And Mirrors

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.

DivAlloc
Source: Portfolio Visualizer

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.

And you would be wrong.

Check it out.

DivGrowth
Source: Portfolio Visualizer

 

DivMetrics
Source: Portfolio Visualizer

From a statistical point of view these portfolios behave virtually identically. (Feel free to noodle around with the data yourself) To the extent there are differences here they are probably just random noise.

How can this be?

It’s because correlations across these assets are high.

DivCorr
Source: Portfolio Visualizer

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.

LMFAO.

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.

So what am I driving at here?

Commit to your shots.

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.