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.

1Q19 Expected Returns Update

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.

1q19sp500er
Data Source: Federal Reserve

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.

The Permanent Portfolio In Action

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.

LeveredPP052019Portfolios
Source: Portfolio Visualizer
LeveredPP052019Monthly
Source: Portfolio Visualizer

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.

Permanent Portfolio Q&A

Last week’s permanent portfolio post generated some great questions and feedback, so I wanted to do a follow-up post addressing some of the most common issues raised.

That’s a big allocation to gold. What about using REITs instead of gold?

Admittedly, gold has a lot of issues as an asset. The biggest issue with gold is that it’s a negative carry asset. Not only is there no yield on gold, but there are also costs associated with storing it (fun fact: your primary residence is also a negative carry asset unless you rent out a room or two).

In theory, it would make a lot of sense to allocate to REITs in place of gold. In an inflationary environment, the real value of the properties would increase while the real value of any debt on them would decrease.

I was able to pull US Equity REIT return data from NAREIT back to 1972 and run a new backtest looking at two different approaches to a REIT allocation. (h/t to @IrvingFisher15 for pointing me to this data on Twitter) The first portfolio swaps half the gold allocation for REITs. The second portfolio swaps half the US equity exposure for a dedicated allocation to REITS. I compared both to a 100% US Equity allocation.

PPREIT
Source: Portfolio Visualizer
PPREITEQ
Source: Portfolio Visualizer
USMKT
Source: Portfolio Visualizer
PPREIT_Growth
Source: Portfolio Visualizer
PPREIT_BACKTEST
Source: Portfolio Visualizer

By swapping some gold for REITs you improve the portfolio’s return and volatility profile but at the cost of greater drawdowns and greater correlation with the US equity market.

REITDrawDown
Source: Portfolio Visualizer

To me, a decision on this comes down to each investor’s preferred risk exposures.

In a barbell approach to portfolio construction such as the one that I favor, I would opt not to replace gold with REITs, because the whole point is to mitigate drawdowns in the “core” sleeve of the portfolio. The opportunistic sleeve of the portfolio will necessarily contain a significant amount of equity risk. This may include real estate exposure.

Someone who is implementing the permanent portfolio as a standalone portfolio, however, would likely prefer the return profile where REITs replace some of the gold.

In the basic permanent portfolio, there’s not enough equity exposure.

Usually I find when people say “there’s not enough equity exposure” what they’re really saying is “the CAGR is too low relative to my return hurdle.” We’ve been conditioned to believe that when CAGRs are too low the only solution is to take more equity risk. But that’s not necessarily true.

This is where the leveraged permanent portfolio concept comes into play. To illustrate what this might look like for a DIY investor, I backtested a simple implementation of a leveraged permanent portfolio.

Portfolio #1 is a 50/50 allocation to PIMCO StocksPLUS and GLD. The PIMCO fund uses a bond portfolio to collateralize a 100% net long exposure to S&P 500 futures for 200% notional exposure. So, at the portfolio level, this portfolio is 50% bonds, 50% stocks and 50% gold for 150% notional exposure.

Portfolio #2 is a 100% allocation to SPY as an investable proxy for the S&P 500.

Vanguard Balanced Index is included as an investable proxy benchmark for a traditional 60/40 allocation.

Below are the results.

LeveredPPGrowth
Source: Portfolio Visualizer
LeveredPPReturns
Source: Portfolio Visualizer
LeveredPPRolls
Source: Portfolio Visualizer

While this is a relatively short time period, I find the results quite compelling. The leverage allows you to increase portfolio returns without adding equity exposure. While the addition of leverage does increase portfolio drawdowns, you’ve gotten a slightly better return than a 100% SPY portfolio with drawdown characteristics similar to a 60/40 portfolio. And again, in the bargain you’re much better protected from an inflationary regime than you would be using either of the alternatives.

One of the most significant shifts in my thinking around asset allocation over time has been to embrace the use of a modest amount of leverage to build more diversified portfolios that are still capable of meeting investors’ return hurdles. I guess I am slowly but surely transforming into a risk parity guy. Of course, the REIT-for-gold switch discussed earlier in this post is also a form of levering a portfolio (REITs are leveraged assets).

Anyway, I’d be remiss to move on without commenting on what I believe is the biggest issue with implementing a permanent portfolio, either levered or unlevered, for an actual client. Particularly a retail advisory client. The issue is that the portfolio massively underperforms equity markets in strong bull markets. So it’s absolutely critical a permanent portfolio investor remain focused on absolute returns in these types of environments. Otherwise, envy will lead to FOMO and FOMO to bailing out of the strategy at EXACTLY the wrong time.

The permanent portfolio truly shines when equity markets are getting hammered, either due to inflation or deflation. It’s not a sexy way to generate returns. The behavioral challenges this presents for investors should not be underestimated.

And for what it’s worth, I don’t think there’s a “solution” for this. Either people are willing to accept the potential opportunity costs of the strategy and cultivate the discipline necessary to stick with it through thick and thin, or they’re not.

What about replacing the gold allocation with trend following or Bitcoin or other uncorrelated alternatives?

By all means! Knock yourselves out. Gold was merely the easiest uncorrelated alternative for me to backtest, and also (probably) the easiest for the DIY investor or retail financial advisor to actually implement at this time. Furthermore, it doesn’t require the investor to bet on a specific investment manager to implement.

But I think it’s perfectly valid to replace the gold allocation with other uncorrelated alternatives. A word of caution, however: in my view the use of other alternatives should be biased toward strategies that perform well specifically in inflationary market regimes. That’s the whole point of owning gold here.

Why no credit exposure?

As alluded to above, this exercise was based on the K.I.S.S principle (Keep It Simple, Stupid). I have mixed feelings about how best to integrate credit in a permanent portfolio. Investment grade credit probably has a home in the bond bucket, though it will introduce a bit more equity-like sensitivity to deflationary conditions.

The lower down the credit quality spectrum you go, or the more you get into hybrid securities like preferred stocks, the more you take on equity-like risk. So to the extent assets such as high-yield debt and bank loans and preferred stocks have a place in the permanent portfolio, it’s actually in the equity bucket.

The permanent portfolio is all about balancing risk exposures in light of their potential patterns of correlation across different macroeconomic and financial market regimes. Asset classes get sorted into buckets based on their historical sensitivities to those regimes and (hopefully) how robust those relationships may prove to be in the future.

This is precisely the same intuition that underlies most flavors of risk parity, including Bridgewater’s famous All-Weather portfolio. The advantage Bridgewater and other large investors have here is that they have access to the full toolbox of financial instruments for portfolio construction. Smaller investors have to hack something together based on the investments they can access.