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

don-draper-wide

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.

The Ultimate Guide To Personal Finance

A friend emailed me this excellent guide to personal finance today. If you are anything like me you will admire it for its parsimony. Email text below in italics. I have lightly edited the text to remove some personal commentary.

 

The personal finance game is three steps:

Step 1 = Earn Money
Step 2 = Spend Less Than Money Earned
Step 3 = Use The Difference To Increase Money Earned, Or To Decrease Money Spent 

Earn Money

  • Think about your career.  Try to advance it so that you earn more money.
  • Take a second job to earn more money. (Side hustle might be the best marketing trick ever. Side hustle = second job)
  • Invest.

Spend Less

  • 101 Ways To Live Like A Poor Hermit.
  • Pay less tax.

Using The Difference

  • Invest.
  • Pay down debt. (this is the same as investing, IMO, it’s all allocating capital effectively)

 

OK I’m back. At an even higher level you can sacrifice a bit of resolution and summarize the personal finance game as:

Step 1: Generate Free Cash Flow

Step 2: Allocate Capital Effectively

All (and yes I seriously mean all) financial problems, for all entities other than sovereign governments issuing fiat currency, reduce down to issues with free cash flow generation and/or capital allocation. Thus, beyond the time value of money and opportunity cost, free cash flow and capital allocation are probably the most important concepts in all of finance.

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.

ET Note: Every Shot Must Have A Purpose

My latest note for Epsilon Theory is a golf lesson we can apply to our portfolios.

The most grievous portfolio construction issues I see inevitably seem to center on basic issues of strategy and commitment. Particularly around whether a portfolio should be built to seek alpha or simply harvest beta(s).

You don’t have to shape your shots every which way and put crazy backspin on the ball to break 90 in golf. Likewise, not every portfolio needs to, or even should, strive for alpha generation.

There are few things more destructive (or ridiculous) you can witness on a golf course than a 20 handicap trying to play like a 5 handicap. And it’s the same with portfolios. For example, burying a highly concentrated, high conviction manager in a 25 manager portfolio at a 4% weight. Or adding a low volatility, market neutral strategy to an otherwise high volatility equity allocation at a 2% weight.

Click through to Epsilon Theory to read the whole thing.

vandevelde.PNG

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.