We Need To Talk About Multiple Contraction

In light of recent market moves I wanted to revisit this post about discount rates and how they might impact valuation multiples (spoiler: it’s an inverse relationship). I think the post holds up pretty well. The key feature was this chart:

118_Implied_Cost_Equity_SP
Data & Calculation Sources: Professor Aswath Damodaran & Michael Mauboussin

Recall that there’s an inverse relationship between the discount rate and the multiple you should pay for an earnings stream. The discount rate has two components:

1) a riskfree interest rate representing the time value of money (usually proxied by a long-term government bond yield), and

2) a “risk premium” meant to account for things like economic sensitivity, corporate leverage and the inherent uncertainty surrounding the future.

Back in January I wrote:

By way of anecdotal evidence, sentiment is getting more and more bullish. Every day I am reading articles about the possibility of a market “melt-up.” If the market melts up it may narrow the implied risk premium and further reduce the implied discount rate. If this occurs, it leaves investors even more exposed to a double whammy: simultaneous spikes in both the riskfree interest rate and the risk premium. The years 1961 to 1980 on the chart give you an idea of how destructive a sustained increase in the discount rate can be to equity valuations.

I am reminded of this as I read this post from Josh Brown, featuring the below chart:

daily-shot-sp-attribution
Source: Bloomberg via WSJ Daily Shot & Reformed Broker

Taken together, this is a fairly vivid illustration of why the current level of corporate earnings matters far less than long-run expectations for margins, growth, and (perhaps most importantly) the discount rate.

(You do remember what’s happening with interest rates, don’t you?)

Assuming no growth, a perpetual earnings stream of $1 is worth $20 discounted at 5%.

Raise that discount rate to 10% and the same earnings stream is worth $10.

Raise it to 15% and the value falls below $7.

And so on.

Now, there’s no way to reliably predict what the discount rate will look like over time. The real world is not as simple as my stylized example. On top of that the discount rate is not something we can observe directly. The best we can do is try to back into some estimate based on current market prices and consensus expectations for corporate earnings.

So, what’s the point of the exercise?

First, I don’t think it’s unreasonable to expect some mean reversion when the estimated discount rate seems to lie at an extreme value. And yes, I counted short-term rates at zero percent as “extreme.” As the above attribution chart clearly demonstrates, multiple contraction can be quite painful.

Second, this should explicitly inform your forward-looking return expectations. Generally speaking, the higher the implied discount rate, the higher your implied future returns. There is good economic sense behind this. “Discount rate” in this context is synonymous with “implied IRR.” To look at a 5% implied IRR and expect a 20% compound return as your base case makes no economic sense. Assuming the implied IRR is reasonably accurate, the only way that happens is if you sell the earnings stream to a greater fool at a stupidly inflated price.

Do fools buy things at irrationally high prices?

Yes. Indisputably. All the time.

Does that make price speculation a sound investment strategy?

No, it does not.

“Multiple expansion” is just a fancy way of saying “speculative price increase.”

Likewise, “multiple contraction” is a fancy was of saying “speculative price decline.”

Private Credit Stats

Sometimes I hear people say we “deleveraged” following the 2008 financial crisis. Sure, the consumer may have deleveraged, but I can assure you there’s plenty of debt left sloshing around in the system. A bunch of it has moved from bank balance sheets to what can loosely be thought of as the hedge fund space.

We call this private credit or direct lending. It’s huge right now in the institutional investing world. Sometimes it feels like every scrappy hedge fund guy in the world is launching a private credit vehicle.

Today, I came across a great paper by Shawn Munday, Wendy Hu, Tobias True and Jian Zhang providing an overview of the space. If, like me, you’ve been inundated with pitch decks from private credit funds over the past couple years, you won’t find much in the way of new information. But the stats are worth perusing.

private_credit_aum
Source: Munday, et al
private_credit_Commits
Source: Munday, et al
pooled_MOICs_IRRs
Source: Munday, et al
IRR_by_vintage_year
Source: Munday, et al

If you are an allocator this is nice base rate data for the space. According to the pitch decks everyone is targeting a net IRR in the mid-teens. It doesn’t surprise me that the median IRR for the post-crisis era is closer to 10% than mid-teens.

Personally, I’m inclined think median returns over the next decade will look more like the 2006 and 2007 vintages. Anecdotally, I can tell you there are middle market deals getting done out there with six turns of leverage and 7% yields. Unless you’ve got warrant coverage, you’re not getting anywhere near 15% IRR on a deal like that.

That kind of behavior reeks of yield chasing.

And we all know what happens to yield hogs.

Eventually, they get turned into bacon.

3Q18 US Factor Returns

Below are my factor return charts, updated for 3Q18. As always, this data lags by one month, so it is technically through August 31. Note also that the more recent bout of market volatility lies outside this date range. It will fall inside the next update.

This one features more of the same. Returns to Market and Momentum continue to grind higher, leaving the more value-oriented factors in the dust.

At bottom I’ve added a snap of Research Affiliates’ latest factor valuations. They’re about what you’d expect given the return data, with Illiqudity (think VC and private equity) and Momentum at the high end of their historical ranges. Value remains at the low end.

It’s worth asking: what’s the point of this exercise?

To better understand and contextualize the following (thanks Rusty Guinn):

[T]here is no good or bad environment for active management. There are good or bad environments for the relatively static biases that are almost universal among the pools of capital that benchmark themselves to various indices.

For a diversified portfolio, the variation in returns is explained almost entirely by the aggregate factor exposures. You’d be surprised how many professionals are ignorant of this.

Now, some of that ignorance is deliberate. There’s a reason investment managers don’t often show clients factor-based attribution analyses. The data typically supports the idea that a significant portion of their returns come from the relatively static biases (“tilts”) mentioned above.

As an allocator of capital, it behooves you to be intentional about how your portfolios tilt, and how those tilts manifest themselves in your realized performance. This self awareness lies at the heart of a disciplined and intentional portfolio management process.

3Q18_rolling_avg_factor_returns
Source: Ken French’s Data Library & Demonetized Calculations
3Q18mkt
Source: Ken French’s Data Library & Demonetized Calculations
3q18size
Source: Ken French’s Data Library & Demonetized Calculations
3q18val
Source: Ken French’s Data Library & Demonetized Calculations
3q18mo
Source: Ken French’s Data Library & Demonetized Calculations
3q18_op_profit
Source: Ken French’s Data Library & Demonetized Calculations
3q18inv
Source: Ken French’s Data Library & Demonetized Calculations
201810_RAFI_Factor_Valuations
Source: Research Affiliates

Shenanigans! “Index Investing Distorts Valuations” Edition

Here is an oft-repeated meme that has begun to grind my gears. Here it is again, from the FT’s Megan Greene:

Passive investments, such as exchange traded funds (ETFs) and index funds, similarly ignore fundamentals. Often set up to mimic an index, ETFs have to buy more of equities rising in price, sending those stock prices even higher. This creates a piling-on effect as funds buy more of these increasingly expensive stocks and less of the cheaper ones in their indices — the polar opposite of the adage “buy low, sell high”. Risks of a bubble rise when there is no regard for underlying fundamentals or price. It is reasonable to assume a sustained market correction would lead to stocks that were disproportionately bought because of ETFs and index funds being disproportionately sold.

The idea that index investors cause valuation disparities within indices is a myth. It is mainly trotted out in difficult client conversations about investment performance. For the purposes of this discussion, let’s restrict the definition of “passive” to “investing in a market capitalization weighted index,” like the Russell 2000 or S&P 500. There is a simple reason everyone who makes this argument is categorically, demonstrably wrong:

INDEX FUNDS DON’T SET THE RELATIVE WEIGHTS OF THE SECURITIES INSIDE THE INDEX. 

It is correct to say things like, “S&P 500 stocks are more expensive than Russell 2000 stocks because investors are chasing performance in US large caps by piling into index funds.”

It is correct to say things like, “S&P 500 stocks are more expensive than OTC microcaps outside of the index because investors are chasing performance in US large caps by piling into index funds.”

It is demonstrably false to say, “NFLX trades at a ridiculous valuation relative to the rest of the S&P 500 because investors are chasing performance in US large caps by piling into index funds.”

Index funds buy each security in the index in proportion to everything else. Their buying doesn’t change the relative valuations within the index.

Here is how index investing works:

1. Active investors buy and sell stocks based on their view of fundamentals, supply and demand for various securities, whatever. They do this because they think they are smart enough to earn excess returns, which will make them fabulously wealthy, The Greatest Investor Of All Time, whatever. As a result, security prices move up and down. Some stuff gets more expensive than other stuff.

2. Regardless, the markets are not Lake Wobegon. All of the active investors can’t be above average. For every buyer there is a seller. If you average the performance of all the active investors, you get the average return of the market.

3. Index investors look at the active investing rat race and think, “gee, the market is pretty efficient thanks to all these folks trying to outsmart each other in securities markets. Let’s just buy everything in the weights they set. We are content to get the average return, and we will get it very cheaply.”

4. Index investors do this.

Despite what we in investment management like to tell ourselves, index investors are rather clever. They are trying to earn a free lunch. They let the active investors spend time, money and energy providing liquidity and (to a much lesser extent) allocating capital for real investment in primary market. They just try to piggyback on market efficiency as cheaply as possible.

In my experience, some index investors like to think of themselves as somehow acting in opposition to active managers. This, too, is demonstrably and categorically false. Index investors’ results are directly and inextricably tied to the activity of active investors setting the relative weights of securities within the indices. That activity is what determines the composition of the index portfolio.

It’s a symbiotic relationship. Index investing only “works” to the extent the active investors setting the relative weights within the index are “doing their jobs.” When the active managers screw up, bad companies grow in market capitalization and become larger and larger weights in the index. At extremes, the index investor ends up overexposed to overvalued, value-destroying businesses. This happened with the S&P 500 during the dot-com era.

Now, flows of passive money certainly can distort relative valuations across indices, and, by association, across asset classes. When it comes to asset allocation, almost everyone is an active investor. Even you, Bogleheads. Otherwise you would own something like ACWI for your equity exposure.

A running theme of this blog is that there are no free lunches. Someone is always paying for lunch. These days, it’s the active managers. But that doesn’t mean the passive folks will always enjoy their meal.

The Incredible Flattening Yield Curve

This is a pretty amazing image, courtesy of J.P. Morgan Asset Management:

2018_0630_US_Yield_Curve
Source: J.P. Morgan Asset Management (obviously)

People are really starting to worry the Fed is going to invert the curve. Historically, an inverted curve (short rates above long rates) has been a pretty good recession indicator. I don’t have a particularly strong opinion about the direction of interest rates, especially now that we are above 2% on the 2-Year. But I do think this chart is telling us something.

If the curve is basically flat from 7 years on out to 30 years, that is not exactly a ringing endorsement of long-term growth and inflation prospects. I’ve heard from some fixed income people that it’s demand for long-dated paper from overseas buyers holding the 30-year yield down. I’ll buy that. But it’s still telling us something about supply and demand for capital along various time horizons.

Namely: we’ve got an awful lot of long duration capital out there looking for a home, and not enough opportunities to absorb it all.

2Q18 US Factor Returns

Below are my latest factor return charts. I update these on quarterly intervals but the underlying data, from Ken French’s Data Library, lags by a month.

Not much to write home about this quarter. The divergence over the past few years between the Momentum and Market factors and the remaining, more value-oriented factors (Value (Price/Book), Operating Profitability, Conservative Investment) remains striking.

The Size factor has also performed well year-to-date. May was a particularly good month for Size (+4.78%) and Momentum (+4.02%). In traditional “style box” terms, this corresponds to small cap growth stocks.

2Q18_Factor_Averages
Source: Ken French’s Data Library
2Q18_Market
Source: Ken French’s Data Library
2Q18_Size
Source: Ken French’s Data Library
2Q18_Value
Source: Ken French’s Data Library
2Q18_Momentum
Source: Ken French’s Data Library
2Q18_Profitability
Source: Ken French’s Data Library
2Q18_Investment
Source: Ken French’s Data Library

Nerd Stuff: Factor Valuation Edition

I have to give Research Affiliates some serious props for their online interactive (and, yes, free tools). I mentioned the asset allocation tool in a post from earlier this week. If you didn’t check out the tool then, you really should.

I did not realize until this morning that Research Affiliates also has a similar tool for factors, called Smart Beta Interactive. This allows you to slice and dice factor strategies and also the underlying factors themselves. I highly recommend checking this one out out, too.

Anyway, this post isn’t meant to be a Research Affiliates commercial. Instead, this is going to be a post on reflexivity. Behold, factor valuations for the US market:

RAFI_1Q18_Factor_Valuations
Source: Research Affiliates

Regarding their methodology, Research Affiliates states:

Just like stocks, bonds, sectors, countries, or any other financial instrument, equity factors and the strategies based on them can become cheap or expensive. We measure relative valuations of the long vs. short sides to estimate how cheap or expensive a factor is. We find that when relative valuation is low compared to its own history, that factor is positioned to outperform. When valuation is high it is likely to disappoint.

This is reflexivity in action. Briefly, reflexivity is a concept popularized by George Soros. The idea is that by taking advantage of perceived opportunities in the markets, we change the nature of the opportunities. Howard Marks likens this to a golf course where the terrain changes in response to each shot.

Here’s how this happens in practice:

Step 1: Someone figures out something that generates excess returns. That person makes money hand over fist.

Step 2: Other people either figure the “something” out on their own or they copy the person who is making money hand over fist.

Step 3: As people pile into the trade, the “something” becomes more and more expensive.

Step 4: The “something” becomes fairly valued.

Step 5: The “something” becomes overvalued.

Step 6: People realize the “something” has gotten so expensive it cannot possibly generate a reasonable return in the future. If prices have gotten really out of hand (and particularly if leverage is involved) there will be a crash. Otherwise future returns may simply settle down to “meh” levels.

Step 7: As the “something” shows weaker and weaker performance, it gets cheaper and cheaper, until some contrarian sees a high enough expected return and starts buying. The cycle then repeats. Obviously these cycles vary dramatically in their magnitude and length.

I do not consider myself a quant by any means, but I think the two most important things for quants to understand are: 1) why a factor or strategy should work in the first place, and be able to explain it in terms of basic economic or behavioral principles; 2) reflexivity.

Many people believe AI is going to push humans out of the financial markets. There is some truth in this. Big mutual fund companies that have built businesses on the old “style box” approach to portfolio construction are in trouble. The quants can build similar funds with more targeted exposures, in a more tax efficient ETF wrapper, and with lower expenses.

What I think people underweight is the impact of reflexivity. If the AIs aren’t trained to understand reflexivity, they will cause some nasty losses at some point. Personally, I think there will be an AI-driven financial crisis some day, and that it will have its roots in AI herding behavior. We are probably a ways away from that. But technology moves pretty fast. So maybe it will come sooner than I think.

Anyway, back to factor valuations.

What stands out to me is Momentum and Illiquidity at the upper ends of their historical valuation ranges. On the Momentum side this is stuff like FANG or FAANMG or whatever the acronym happens to be this week. On the Illiquidity side it’s private equity and venture capital. If you have read past posts of mine you know I believe most private equity investors these days are lambs headed to slaughter.

There tends to be a lot of antipathy between quant and fundamental people. Even (perhaps especially) if they are co-workers. The fundamental people are afraid of the quants. Partly because they are afraid of the math (a less valid fear), and partly because they see the quants as a threat (a more valid fear). Quants, meanwhile, tend to believe the fundamental people are just winging it.

In reality I think this is more an issue of language barrier and professional rivalry than true disagreement over how markets work or what is happening in the markets at a given point in time. In my experience, the best fundamental investors employ quant-like pattern recognition in filtering and processing ideas. Many quants, meanwhile, are using the same variables the fundamental people look at to build their models.

Personally, I think anyone who wants to survive in the investment profession over the next twenty years is going to have to be something of a cyborg.

Though, come to think of it, that probably applies to every industry.