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).

*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.

ET Note: What You Call Love

My latest Epsilon Theory note is about the seemingly obviously nonsensical idea that “words can be violence.”

[I]n case you’re wondering, no, words are not equivalent to physical violence. That is nonsense.

What is not nonsense is the notion that if you can deftly manipulate the symbols people use to assign and create meaning in their lives, you can manipulate their thoughts and behavior. We have a name for this outside academia and the culture wars.

It’s called advertising.

Read the whole note on Epsilon Theory.