Figaro

The dancers on stage are flopping around, dancing awkwardly in the absence of any music. They look uncomfortable as the Emperor Joseph II enters the rehearsal for the first performance of Mozart’s The Marriage of Figaro.

Emperor Joseph II: What is this? I don’t understand. Is it modern?

Kappelmeister Bonno: Majesty, the Herr Director, he has removed a balleto that would have occurred at this place.

Joseph: Why?

Count Orsini-Rosenberg: It is your regulation, Sire. No ballet in your opera.

Joseph:  Do you like this, Salieri?

Antonio Salieri: It is not a question of liking, Your Majesty. Your own law decrees it, I’m afraid.

Joseph:  Well, LOOK at them! No, no, no! This is nonsense. Let me hear the scene with the music.

Amadeus (1984)

Of all the investment strategies that force investors to hold their noses and take their medicine, we are most uncomfortable with those based on historical price movements. We know that they work, up to a point. And so we balance in our heads the ideas of participating in trends with some vague notion that we will make enough money doing so to compensate us for it all blowing up in our face one day. Alternatively we hope that we will be able to buck the trend before it reverses, whether through a contrarian analysis of price movements or some statistical model of investor behavior. Even when the models work, it can feel unsettling, like dancing a ballet without music.


Music’s exclusive function is to structure the flow of time and keep order in it.

Igor Stravinsky, as quoted by Geza Szamosi in The Twin Dimensions: Inventing Time and Space

In a Three-Body Market, narratives are the music. Understanding how they influence the structure and flow of price-trending behaviors is not a cure-all. But it can be a useful tool.


If you would dance, my pretty Count, I’ll play the tune on my little guitar. If you will come to my dancing school I’ll gladly teach you the capriole. I’ll know how; but soft, every dark secret I’ll discover better by pretending. Sharpening my skill, and using it, pricking with this one, playing with that one, all of your schemes I’ll turn inside out.   Se vuol ballare, signor contino, il chitarrino le suonerò, sì, se vuol venire nella mia scuola, la capriola le insegnerò, sì. Saprò, saprò, ma piano, meglio ogni arcano dissimulando scoprir potrò. L’arte schermendo, l’arte adoprando, di qua pungendo, di là scherzando, tutte le macchine rovescerò.
  • The Marriage of Figaro, by Wolfgang Amadeus Mozart from a libretto by Lorenzo da Ponte (1796)

If narratives are the music, we must be conscious of the musicians.


This is Part 4 of the multi-part Three-Body Alpha series, introduced in the Investing with Icarus note. The Series seeks to explore how the increasing transformation of fundamental and economic data into abstractions may influence strategies for investing – and how it should influence investors accessing them. 

  Economic Models Behavioral Models Idiosyncratic Models
Systematic Security Screening Econometric GTAATrend-Following
Momentum
Value Factor Investing
Mean-Reversion
Statistical Arbitrage
High Frequency  
Discretionary DCF / DDM / Price Target
Quality-Based
Credit Work
Growth Equity
Relative Value
Asset Value
Sentiment Value + Catalyst Discretionary Macro
Other Trading Strategies
Activism Distress

Trend-following is an odd little corner of the market.

Well, not little, I suppose. When taken in the aggregate, trend-following strategies – by which I include all strategies which use historical price behavior as a primary component in determining current positioning – account for at least $400 billion, and some multiple of that in exposure. If we included all the momentum-inclusive quant equity strategies and related factor portfolios, too, we’re easily wandering into the trillions.

And yet it still has an uneven reputation.

It wasn’t long ago that the most reputationally aware institutional money (i.e. endowments and foundations) wouldn’t touch anything that looked like it was trading based on price movements. Some still don’t. It was considered this sort of uncouth thing, a place for daytraders and charlatans. The real adults were investors! Value investors, business buyers, participants in the process of setting the proper price of capital! It didn’t help, of course, that many of the go-go momentum shops of the late 90’s were pretty sloppy, or that many so-called trend-following strategies were just some guy drawing dumb lines on a Bloomberg chart. And then later getting a computer to draw dumb lines for him.

Now, the empirical premises of the most basic trend-following strategies are not really all that much in question. They work. The data are pretty clear that they work. Of course, don’t tell that to the professor at Wharton who taught me 18 years ago that technical analysis was only so much superstitious hogwash. And yes, as much as we might protest that the reversal pattern of long-term underperformers that DeBondt and Thaler identified in 1985 or the short-term trend continuation pegged by Jegadeesh and Titman in 1996 are different, it is still technical analysis, y’all.

So yes, it works. But investing because of how the price has moved doesn’t FEEL like investing, and this feeling is a hump that a lot of investors still can’t get over. It’s too simple. As it happens, a lot of professional investors are really uncomfortable telling their clients and boards that they buy things just because they’re going up and sell them just because they’re going down. This is a predictable outcome, given that many of those professional investors have sold themselves to their clients and boards on the basis of, y’know, not being the kind of poor sap who just does what everyone else has been doing.

And so it is that there are all sorts of stories about why trend-following and momentum strategies work that are meant to lend them credibility. Maybe it’s because dispersion of fundamental information takes time. Maybe it’s because we overextrapolate earnings growth. Maybe it’s because price trends are really just a proxy for intangible business momentum.  I’m sure there are many very bright people who earnestly believe these stories. Hell, they may even be right. But for my money, the simpler explanation – and to be fair, it’s one that is usually recognized by those proposing the other explanations – is the easier one. Things that go up feel better and safer, a natural emotion that we have institutionalized through Morningstar ratings, consultant buy lists, ‘approved lists’ and hyper-frequent portfolio reviews designed under the auspices of weeding out ‘bad investments’, by which we mean ‘investments that have done poorly over the last 12-24 months.’

The problem is that while most of us can get our heads around why price and performance trends ought to continue, we also know that they can’t and don’t continue indefinitely. We also know that, if value investing works, too – and it does – there’s a point at which our view probably ought to shift to an expectation of a contrary relationship between future returns and stocks with strong historical performance. As you might imagine, there are a lot of implementation choices here. In fact, I’m not sure there is a space that provides the potential for as much diversity in signal design as trend-following.

Lest you get too frightened (or excited), no. This isn’t going to be a survey piece. There are great primers available on trend-following, and I’m not going to write a better one. If you’re in the market for a survey course in investment strategies, AQR’s Antti Ilmanen wrote the Bible. I’d also add that if you aren’t following the work by Corey Hoffstein at Newfound Research, you may find it even more useful. He researches implementation questions in the open, and since doing is invariably the best way to learn, I suspect you will gain immeasurably from following along. I have.

If you do want to understand the smorgasbord of strategies which incorporate price as an input, I do have a small number of suggestions, none of which is groundbreaking, and all of which would be a standard part of the arsenal of questions to ask any trend, CTA, managed futures or systematic macro fund manager:

  1. Understand the difference between time-series momentum and cross-sectional momentum, and know which your managers are relying on. They perform more differently than you would expect.
  2. Understand time horizon diversity among the signals being used, and how you might expect those signals to work differently.
  3. Understand how non-price data is being used in models, as primary signals or conditioners.
  4. Understand how positions are sized, and how gross and net exposures are managed.

I’m not making recommendations here, but at their very likely great distress, I’ll also share the names of a few people who, in my experience, are preternaturally good at discussing the hows and whys of these strategies. And this is me suggesting, not them offering, y’all:

  • Ewan Kirk at Cantab Capital, to explain anything trend-following or managed futures.
  • Rob Croce at BNY Mellon (and in full disclosure, a former colleague), to sell you on the religion of pure trend.
  • Jason Beverage at Two Sigma, to explain shockingly complicated quant portfolio construction concepts in ways you will understand.
  • On anything on the shorter end of the time-horizon spectrum (where a lot of mean reversion strategies live), you will never go very wrong by asking your AQR rep for a chat with Michael Mendelson.

But again, the intent behind this piece – as with the rest of the series – isn’t to tell you how and why these strategies work. It is to discuss whether we think a more abstracted market with greater always-on awareness of what other investors are thinking and doing ought to change the way these strategies work. The short answer? Yes. It should also change the way some of the strategies are designed and incorporated into portfolios.

What we are NOT talking about is parsing the news for sentiment. Sure, tone and sentiment are a component of any narrative. But a lot of money has been spent by hedge funds and others over the last decade and a half to mine news for sentiment, first by building huge manual research teams in India, and later by assigning those tasks to computers. Most of those efforts have been huge flops. The relationship between narrative and price trends is different, and I want to show you why.

This foray will necessarily cover the relationship between narrative and trend-following more generally, and not with individual strategies. After all, if we’re going to tell stories about how prices dance to the music of the stories that Wall Street tells, we must hold some things constant. So let me tell you a story about just three companies. They existed over the last 3 years. Conveniently, all of them are called Tesla Motors.

A Ballet in Three Acts, Act I: Resilient Tesla

Since calling them all the same thing will be confusing, let’s call the first of our companies “Resilient Tesla.” Resilient Tesla existed between for six months, from November 2016 to May 2017. In what will be familiar to regular readers, we rely on natural language processing technology from Quid to relate and graph news articles from a very broad universe of sources based on content, context, phrasing and sentiment. We provide some of our own characterizations of the clustered content to aid interpretation.

What did media reports have to say about Resilient Tesla? Well, they didn’t ignore bad things that were going on. Media reported on issues with autopilot, and on reported safety issues. It reported on issues with dealership access in states. But those stories were curiously isolated. With few exceptions, they shared no language or other similarities with the core of the conversations taking place about Tesla.

The center of gravity around which the Resilient Tesla narrative orbitted was management guidance, in particular around Tesla’s desire to raise capital to grow. The stories about management guidance and capital raising were usually also stories about the Model 3 launch. They were also about Tesla taking over as the most valuable carmaker in the US. Importantly, they were also stories about Wall Street positioning, and what big investors and sell side analysts thought about the company. And – for the most part – the tone and sentiment of all of these categories were good and positive. What is more, all of these linked positive topics were things where Tesla was the only game in town. That lent stability to the overall narrative, and that narrative was growth. We need capital, but we need it to launch our exciting new product, to grow our factory production, to expand into exciting Semi and Solar brands. Sure, there were threats, but always on the periphery.

Source: Quid, Epsilon Theory

Resilient Tesla was a positive trending stock. Over all but short bursts over this period, it would have been a long position for most long-, medium- and short-term models. Sure, models and strategies incorporating liquidity, volume, daily price action, large block trade activity or other esoteric anomalies may have had different exposure, but Resilient Tesla was a classic long for most.


Source: Bloomberg, Epsilon Theory. Note: This is not a recommendation to buy or sell any security, or to take any portfolio action. Past performance is not indicative of future results. You cannot invest directly in an index, and we do not invest directly in any individual securities.

A Ballet in Three Acts, Act II: Transitioning Tesla

The stories started changing in summer 2017. Act II tells the story of Transitioning Tesla, a company which existed for three months, from May 2017 through August 2017.


Source: Quid, Epsilon Theory

The overall sentiment and the language used in stories about Transitioning Tesla were still positive. In fact, they were actually slightly more positive than they were for Resilient Tesla. But gone was the center of gravity around management guidance and growth capital. In its place, the cluster of topics permeating most stories about Tesla was now about vehicle deliveries. Articles about Tesla used to be Management says this AND Model 3 is coming AND did you know that Tesla is now the most valuable US carmaker AND here’s Wall Street’s updated buy/sell recommendation stories. For Transitioning Tesla they were The Model 3 launch is exciting AND the performance of these cars is amazing, BUT Tesla is having delivery problems AND can they actually make them AND what does Wall Street think about all this? The narrative was still positive, but it was no longer stable.

In other words, two things happened here:

  • Transitioning Tesla lost control over the narrative. It failed to control its cartoon.
  • The main connectivity among the stories people tell about Tesla became concern about deliveries and production.

We’ve previously described narrative as providing meta-stability to an overall market: the ability to shrug off contrary new facts that are inconsistent with the narrative, and to incorporate new facts that were previously considered tangential. Instead, the excitement about non-Model 3 opportunities like Solar, Gigafactory and Semi moved further to the periphery, less linked to most content about the company. Debt concerns, competition and partnership issues, previously easily shrugged off, were now being mentioned in articles that were ostensibly about something else. This is what it looks like when meta-stability fails. This is what it looks like when the narrative breaks.

You wouldn’t necessarily have sensed a difference in how the Tesla story was being told. It was still positive in tone, still almost universally optimistic. Investors and the public alike were still excited about the vehicles’ uniqueness. They still saw value in the periphery businesses. You probably wouldn’t have thought much of the price performance over these four months, either. Long-term cross-sectional momentum models would have shrugged off the addition of four choppy months of ultimately in-line performance. Time-series models would have scored a still-rising stock.

But it was already broken.


Source: Bloomberg, Epsilon Theory. Note: This is not a recommendation to buy or sell any security, or to take any portfolio action. Past performance is not indicative of future results. You cannot invest directly in an index, and we do not invest directly in any individual securities.

A Ballet in Three Acts, Act III: Broken Tesla

The third Tesla – Broken Tesla – existed between August 2017 and the present.

The growing concern about production and vehicle deliveries entered the nucleus of the narrative about Tesla Motors in late summer 2017 and propagated. The stories about production shortfalls now began to mention canceled reservations. The efforts to increase production also resulted in some quality control issues and employee complaints, all of which started to make their way into those same articles. When stories about suppliers not getting paid were coupled with a failed MBO, writers all too easily related these concepts with the management and oversight of the company. Once writers connect these items, then the previously peripheral issues of autopilot crashes, recalls and union disputes start finding their way in as well.

Now, almost all of these things were obviously very real, very tangible problems. That’s not the point. The point is that there was already broad private knowledge that there were issues with Tesla’s manufacturing process. There was already broad private knowledge that senior finance executives had been leaving the company. There was already broad private knowledge that Elon was eccentric. There was already broad private knowledge about the previously peripheral problems for the company. But none of those things really mattered until they became part of the common knowledge around the stock. Once that happened, a new narrative formed: Tesla is a visionary company, sure, but one that doesn’t seem to have any idea how to (1) make cars, (2) sell cars or (3) run a real company that can make money doing either.

Source: Quid, Epsilon Theory

But that’s all the music. So what is the dance? Well, the performance of the stock in this period is probably familiar. This was a model trade for most trend-followers, especially those with more basic strategies. A long-term positive trend, followed by a flat period to roll off old signals, followed by relatively quick transition to a new trend. The funds incorporating more basic long-term cross-sectional signals only probably got hurt a little in Q3 2017, but have been in the money since then.


Source: Bloomberg, Epsilon Theory. Note: This is not a recommendation to buy or sell any security, or to take any portfolio action. Past performance is not indicative of future results. You cannot invest directly in an index, and we do not invest directly in any individual securities.

The Epilogue

If you’re reading this note on Tuesday, October 23rd, you’ll know that Tesla has moved up its earnings call to tomorrow evening. As of mid-day today, TSLA stock is up about 5.6%. As always, these things are overdetermined, but it’s hard to think that responses to chirps from management about a ‘near-profitable’ quarter and record production and deliveries don’t have something to do with it.

Tesla valuations are built on the basis of phenomenal projected future growth. The idea that anyone is going to update some model assumption after tomorrow’s results and legitimately come up with a massively different valuation is nonsense. And yet. If I were short the stock based on the supportive environment for a continued negative price trend, I’d be looking very closely at the following:

  • Can Elon and team put on a performance that starts to put distance between how media and Wall Street talk about the company in the same breath that they talk about credibility issues for management? Can they stay on-point and look like adults?
  • Will the ‘near-profitability’ story and facts dispel the swirling attachment of debt / cash flow / failed MBO concerns to the principal stories about Tesla?
  • Will they be able to bring the topics that have remained positive (e.g. China production, Panasonic’s progress, even Semi, believe it or not) into the main narrative about the stock?

Perhaps most importantly, can Elon step back into the role of Missionary? Or will he continue to let other people determine his cartoon?

The Story of the Three Acts

Let me address a couple legitimate criticisms of this way of complementing trend-following in advance.

The first is that this all seems very easy to see in retrospect. Would I have seen this in advance? Would you? Not sure. There is predictive power in this, but it is hard. It is also systemizable. It is also a new way of looking at things that requires us to build some new muscles to see clearly – and to avoid the confirmation bias that inevitably creeps into this kind of analysis.

The second criticism – and this one comes up a lot – is that professional investors and analysts don’t make judgments about companies and securities based on the content of news pieces. Assuming that this is a serious observation, I would only respond by recommending that you talk to more fund managers and read more sell side pieces. Still, there is a lot to be gained by understanding how common knowledge and broad private knowledge alike DO differ by and among different groups – from broad media, to specialized media, the sell side, long-only fund managers, hedge fund managers and macro strategists. This is something we are working on expanding as part of our research effort.

The third is skepticism that the existence of what we’re calling narrative can predict the direction of a stock. Well…yeah. I mean, I agree. I’m not at all convinced that it can, and there’s nothing I’ve written here today that should convince us that we could have used this ex ante to bet on or against TSLA. This isn’t about predicting the direction of a stock. It’s about updating our predictions about whether it is an environment more or less conductive to investing with or allocating to various types of trend-following strategies. You may not be able to predict the trend, but you may have some ability to project its stability. It’s about understanding how the music changes the dance. It’s not about the answer, it’s about the process.

What else do we take away from all this? What else do I think?

  • I think allocators should be more actively engaging our trend-following managers to be curious about why their signals work. Ask questions about how they try to develop economic intuition for them. They don’t have to buy into how we are conceptualizing this. But they should be constantly curious.
  • I think I’m more inclined toward simple strategies that are heavy on long-term trend. While abstractions are just as capable of creating choppy periods, I think  conducive environments for long-term trends will be more common. I think the cause will be broader awareness of these tools by CEOs and other missionaries. . That’s pure conjecture on my part. But even if I’m wrong, long-term trend’s traits in major equity market drawdowns are a very nice second prize.
  • I’m less inclined toward the managed futures / CTA behemoths. By definition, I think more adaptable strategies capable of turning off models that aren’t suited for the environment – maybe on similar grounds to what we’re arguing, and maybe on more sophisticated ones – will be the winners. The megashops in this space have created capacity and liquidity through strategy stratification that tether them to relatively more static approaches, or else would force them to significantly reduce risk budgets (which many have already done as they’ve transformed themselves into management fee shops).
  • I think, at the margin, I prefer strategies more heavily driven by absolute time-series momentum vs. cross-sectional strategies, although they are perfectly acceptable complements, as well. Both have a role. But I think the bigger abstractions and narratives will require us to capture beta effects (i.e. I want strategies more capable of making non-offsetting directional bets).
  • As an aside, I think if Elon Musk wants to get this thing back on track, he needs to control his own cartoon and embrace his role as Tesla’s missionary again. Among…uh…a few other things. I’ve never owned the stock and I never will. But unlike most people at this point, I really do want Elon to succeed. Yes, really.
  • Maybe most importantly, we have all intuitively adopted ‘trend-following’ thinking in our normal portfolio construction behaviors. After a pleasant decade for risky assets, most of us have internalized a sense of stability in the trend in something like the S&P 500. It won’t allow you to predict the future. But awareness of narrative stability may help you to understand if and when the narratives supporting the “just keep it simple and buy SPY” heuristics start to break down.

PDF Download (Paid Membership Required): http://www.epsilontheory.com/download/16949/

It Was You, Charley

Charley: Look, kid, I— how much you weigh, son? When you weighed one hundred and sixty-eight pounds, you were beautiful. You coulda been another Billy Conn, and that skunk we got you for a manager, he brought you along too fast.

Terry: It wasn’t him, Charley, it was you. Remember that night in the Garden you came down to my dressing room and you said, “Kid, this ain’t your night. We’re going for the price on Wilson.” You remember that? “This ain’t your night!” My night! I coulda taken Wilson apart! So what happens? He gets the title shot outdoors on the ballpark and what do I get? A one-way ticket to Palookaville. You was my brother, Charley. You shoulda looked out for me a little bit. You shoulda taken care of me just a little bit so I wouldn’t have to take them dives for the short-end money.

Charley: Oh, I had some bets down for you. You saw some money.

Terry: You don’t understand! I coulda had class. I coulda been a contender. I coulda been somebody, instead of a bum, which is what I am. Let’s face it. It was you, Charley.

— On the Waterfront (1954)

Read moreIt Was You, Charley

The Many Moods of Macro

I think the original version of this gag is from a Far Side comic in reference to Irish setters, although I’ve omitted it out of respect for Gary Larson’s wishes. Truth be told, I always felt that Old English Sheepdogs would have had a better case for “creature who looks more or less the same regardless of circumstance” than setters. I guess this is one of those things that is infinitely transferable to whatever kind of dog you had growing up.

Unless your childhood dog was a global macro portfolio manager, however, I suspect the rather monotonic flavor of their returns has puzzled you from time to time. For all its inputs, for all its data packaged together from far-flung corners of the globe, all synthesized into sensible and well-researched models, the typical macro fund’s positioning and success is heavily reliant on a small number of influential drivers and environments.

On the surface that’s not necessarily a bad thing, unless you’re paying a ton for it, which you probably are, even in 2018. After all, repeatability and persistent premia are not bugs, but features that we seek out from systematic investing. But for investors in systematic tactical strategies and global macro hedge funds, the expectation of persistent novel sources of return should be scrutinized. In a Three-Body Market, they should be doubted.

A horse having a wolf as a powerful and dangerous enemy lived in constant fear of his life. Being driven to desperation, it occurred to him to seek a strong ally. Whereupon he approached a man, and offered an alliance, pointing out that the wolf was likewise an enemy of the man. The man accepted the partnership at once and offered to kill the wolf immediately, if his new partner would only co-operate by placing his greater speed at the man’s disposal. The horse was willing, and allowed the man to place bridle and saddle upon him. The man mounted, hunted down the wolf, and killed him. The horse, joyful and relieved, thanked the man, and said: ‘Now that our enemy is dead, remove your bridle and saddle and restore my freedom.’ Whereupon the man laughed loudly and replied, ‘Never!’ and applied the spurs with a will.

— Isaac Asimov, Foundation (1951)

At their core, most macro models are central banking models and macro managers are carry investors. They willingly tied themselves to success in predicting bank actions, and in so doing had a wonderful stretch of good returns and low correlations with stocks. Now that predicting bank action will increasingly require short carry positioning, and now that betting on uncoordinated action has gotten tougher, they’re feeling the spurs. This is your choice, too: buck the rider or feel the spurs.


The impulse to find a “a man who can make a plan work”, from F.A. Hayek’s brilliant Road to Serfdom cartoons, is not just a political one, but infects the way we make decisions as investors. We make portfolio plans ourselves, with our committees or with our advisors, and they…rarely turn out exactly like we wanted.

We never have the best possible portfolio we could have had. There is no decision structure that won’t yield questions of the, “Well, why weren’t we 100% in U.S. growth stocks the last three years?” ilk from our clients. More often, we made some real mistakes. We misread the risk environment and weren’t fully invested for our clients. Knowing we shouldn’t, we gave up on a value strategy in a 7-year drawdown right before sentiment turned. We sold bonds ahead of what we thought were inevitable rate hikes and were wrong for five years.

We know we need to fix these kinds of errors.  Too often our solution is to find the team with a model that understands “how all this madness fits together” and can exploit it for us. That’s the allure of systematic macro and tactical asset allocation. It’s well-intended. It’s also a path paved with peril.


This is Part 1 of the multi-part Three-Body Alpha series, introduced in the recent Investing with Icarus note. The Series seeks to explore how the increasing transformation of fundamental and economic data into abstractions may influence strategies for investing — and how it should influence investors accessing them. 

After Ben wrote The Three-Body Problem, and then again with The Icarus Moment, I suspect I reacted like many other readers. I didn’t have to predict whether I thought asset prices were increasingly driven by abstractions or higher degree derivatives of economic and fundamental data, as Ben argued. I was observing it. But between those observations and the related belief that most alpha-oriented strategies have been forced into deeper levels of the Keynesian Newspaper Beauty Contest — that we are increasingly in the business of predicting what others are predicting others are predicting rather than the impact of changes in real economic facts — I have the same questions: How should this impact our strategies? Our portfolios? The questions we ask our advisers and fund managers?

These questions only matter if this whole state of affairs persists, of course. If there’s a hypothetical criticism you could level at the framework Ben and I are working from, it is that the Narrative-driven market isn’t really a thing, that it’s just a label we are throwing on a period of temporary loopiness created by central bank-driven hyperliquidity. A period that appears to be ending. If you think this is the case, then we’re the guys in burlap sacks on the street corner shouting, “This Time It’s Different” right before things go back to normal. I’m empathetic to the view. I mean, I think the view is wrong, but I’ve heard that people are comforted when you tell them you’re empathetic to their view.

I think it’s wrong because we aren’t just observing this in financial markets. We are observing polarization and quantization — rounding words and numbers to their nearest analog — in nearly every human social sphere. Our media-connected Panopticon converts every uttered word into a loaded ideological message, in which every action is a symbol in service to a Narrative. Yes, central bankers were the original missionaries in our little history, but CEOs, financial media, crypto-experts, senators, regulators, traders and other power brokers are all wise to the game now. So, if you want to tell me we will see a return to a market in which the transmission of economic data and fundamental characteristics of issuers manifests in asset prices over some meaningfully investable period of time, fine. But you’ll have to tell me why you think that’s going to happen in politics, culture and media, too.

The painful Catch-22 for the investment advice industry is that people expect times of uncertainty to be the opportunity for advisers to prove themselves. When I talk to financial advisers and RIAs in times of perceived dislocation in asset prices, they want to know whether they ought to transition some of their stock portfolio to hedge funds. They want to know if now is the time to allocate to long/short equity managers. They want to find someone who can steer exposure to take advantage of dispersion when the dislocation corrects. When geopolitical volatility doesn’t manifest in market risk, the conversation is similar. Investors want a macro strategist with a model that answers how it all fits together. Maybe it’s as simple as adding a tactical asset allocation overlay through one of the big turnkey platforms, or maybe it’s hiring a systematic global macro hedge fund. There’s finally dispersion again, and the beta rally is over — now go be tactical and find alpha!

Want to know why Ben’s notes Tell My Horse and Three-Body Problem each yielded more emails from fund managers, CIOs, pension executives and investment professionals than many notes combined? Because they don’t know exactly what to tell their clients who are looking for macro guidance. Because not only is this environment not turning out to be a goldilocks regime for tactical, cross-asset, alpha-seeking managers, it’s becoming an environment in which even the things that used to work aren’t working for them. At all.  Nowhere is that truer than in strategies sitting at the confluence of what in our framework we are calling Systematic strategies operating Economic models. Don’t believe me? Here’s the very long-cycle trend, seen through the lens of the HFRI Macro: Systematic Diversified Index.

Source: eVestment April 2018. An investor cannot invest directly in an index. For illustrative purposes only.

The systematic universe has a lot of trend-following funds. Many of those have performed quite poorly. But that isn’t all that’s happening here. Even the broader Global Macro category, represented here by the HFRI Macro (Total) Index, looks similar. We could similarly split the systematic category into those focused only on trend-following and those that are not, and it would tell the same story.

Source: eVestment April 2018. An investor cannot invest directly in an index. For illustrative purposes only.

What do we mean by “Econometric GTAA”?

The investment industry loves to obfuscate, and terminology can be a bear. Let’s cut through it.

In Hedge Fund Land, “Macro” — represented in the second chart above — is a term of art. Jargon. It refers to a universe of hedge funds, usually self-labeled, that pursue strategies that mostly allocate across and between different broadly defined assets. The term “Systematic Macro” simply refers to those which do so on a mostly systematic basis. By systematic, I mean that the trades are typically generated based on a system rather than determined by a human. That means different things for different funds, and many will have individual sleeves of the portfolio or elements of the portfolio’s construction that still come under human scrutiny. But in general, these funds trade based on generalized ideas and principles memorialized in code.

Depending on who you are talking to, you will also hear strategists, fund managers or consultants talk about “GTAA”, or Global Tactical Asset Allocation strategies. When a fund manager calls a strategy that allocates across assets GTAA instead of Macro, he usually means that he is willing to sell it to you for a lower price, often means that his trades will be confined to a defined, larger set of simple long and short expressions on broad asset classes, and sometimes means that he will have a general bias toward being long financial markets exposure. This is part of the universe I’m writing about here.

There’s a third category of strategies which are not precisely a sub-set of Systematic Macro, but at least occupy a big, overlapping part of the Venn diagram: Managed Futures and CTAs. This, again, is where terminology gets confusing. Managed Futures and CTAs are technically a structural category, by which I mean that they aren’t so much defined by what they do as by what they are. Because futures contracts were originally an instrument devised to trade commodities more efficiently, this industry and its structures formed around strategies for trading futures contracts on those commodities — which makes sense, since CTA stands for “Commodity Trading Advisor.” Over time, as extraordinarily liquid futures contracts became available in equities and interest rate markets, the CTA structures were able to accommodate strategies that looked almost exactly like what we’d see in a global macro hedge fund. But, as I noted in my quip above, this part of the universe tends to trade more often based on price trends, rather than what’s going on in the economy or in companies and other issuers.

So, we have three heavily overlapping Venn diagrams — Systematic Macro, GTAA and Managed Futures. But this piece is about strategies I’m labeling as Econometric GTAA. What do I mean? I mean strategies which trade long and short across a broad range of markets based on computer models driven by (and sometimes predicting the trajectory of and rates of change in) inflation, interest rates, asset flows, economic growth, corporate margins and earnings more broadly, tax rates, trade policy, balance of payments, and trade deficits, etc. These strategies will find their way into your portfolios in many ways. If you buy a Global Macro hedge fund, you will probably get a lot of this. If you hire a Managed Futures fund, you may get some of this, although as I mentioned, it is more likely to be driven by trend-following. If you buy a Multi-Strategy hedge fund, you will probably get a lot of this. But this isn’t just hedge funds. If you buy a “rotation” or “tactical” strategy from an ETF strategist or Tactical Asset Management Plan (TAMP), you will probably get a lot of this. If you hire a financial adviser from an institution with a home office that recommends asset allocation models, you are probably getting some muted flavors of this. So what do these strategies look like?

What Econometric GTAA Models Look Like

When you hire a “tactical” advisor or portfolio manager, while there is a huge amount of surface-level diversity, what you’re usually getting is some subset of the below:

Illustration of Typical Tactical Asset Allocation Framework

Source: Epsilon Theory April 2018. For illustrative purposes only.

The basic framework here takes in some combination of what I’m calling Econometric Data, Market Data and Sentiment. The mix may differ dramatically. In the case of Managed Futures strategies, many ETF strategists and “Tactical Allocation” funds, and others that call themselves Systematic Macro, models may skew entirely toward Market Data. They may even emphasize price movements above just about every other kind of input. This piece isn’t about those funds, and it isn’t about those strategies. It’s a big topic that deserves its own note, because most of the off-the-shelf model portfolios-in-a-box, ETF-based strategies, sector-rotation strategies and tactical allocation funds rely almost entirely on models driven by Market Data alone, usually simple valuation and momentum models.

But what we’re focused on is that top half — the Econometric Data. The approach that managers and strategists will take to incorporate these data will differ. In some cases, strategies will impute a direct transmission engine between econometric data and (implied) expected asset prices, and their desired position. For example, a strategy may be something as simple as rank-ordering countries by their short-term interest rates, buying the ones with higher rates and shorting the ones with lower rates. There are all sorts of implicit views this expresses on investor asset pricing behavior and risk, but the explicit mechanism for establishing positions connects relative interest rates to relative asset price returns over some period of time. This is what the illustration refers to as Implicit Price Behavior models — “Certain values of variable X will more often than not result in changes in the prices of asset price Y.”

In other cases, one or more (usually more) bits of data will be incorporated with economic logic into an interim model. That interim model will typically represent a more explicit simulation of the behavior of another actor or actor(s). For example, rather than estimate a simple relationship between, say, changes in consensus inflation expectations and whether inflation-linked bonds will outperform nominal bonds, many Econometric GTAA strategies will take in GDP growth, earnings growth, balance of payments, wage growth, producer price momentum, corporate margin and money supply data to predict the pressures on central banks to make changes in interest rate policy. That output would then influence views and positioning on a range of assets.

As with the bulk of tactical asset allocation strategies, ETF models, etc. mentioned earlier, the influence of these interim models or even many of the direct transmission models of Econometric Data to positions is often presented in context of valuation of the underlying assets, and momentum of the price of the underlying asset and/or the model’s signal itself. In other words, a fund may predict the pressure on a central bank to act, but it may be the momentum or change in that variable which produces a tradable signal. Alternatively, a trade may be conditioned on some valuation or momentum state, or even by the state of another interim model (i.e. “We trade when we have confirmation between our geopolitical framework and recent price action.”).

The types of positions these models establish will differ as well. For most strategies — especially those that fancy the GTAA moniker — the views tend to be long/short, and usually asset class neutral. For example, a model might pair a 5% long or overweight position in US stocks with a 5% short or underweight position in, say, German stocks. For others, the views might compare assets with cash. In other words, the models decide whether to have market exposure at all. This question of “directionality” is a big one. It’s one that tends to exaggerate differentness among practitioners of these strategies and pigeon-hole the emphasis of more risk-focused managers into a smaller number of relative value trades between assets.

But What is It, Really?

So with all these inputs, with all this diversity, we have our pick of a lot of interesting multi-asset and macro strategists with a lot of interesting different models, right?

Meh, not so much.

We don’t claim to have some secret sauce for analyzing drivers of fund performance. Most approaches are pretty well-trod ground at this point, although I was tempted to measure facial width-to-height ratios just for fun. But no, we’re simple — boring multi-factor regressions against some basic style and market factors. Fortunately, as is often the case, simplicity tells most of the story. Of the 74 funds in the HFRI Systematic Macro universe, 60 have positive, statistically significant betas to interest rates. Around 40 have betas higher than 1.0. These are betas in a multi-factor context that includes a range of market and traditional style factors. To be fair, many of these funds are implemented through futures or with market neutral positioning that allows them to earn cash returns, but this is a small portion of this effect. In the end, you are roughly three times more likely to run into a Systematic Macro fund with returns that look like a levered bet on interest rate-sensitive instruments than one that neutralized (read: hedged) the aggregate systematic influence of rates on its risk profile over time.

Source: Epsilon Theory, HFR, eVestment April 2018. An investor cannot invest directly in an index. For illustrative purposes only.

I don’t think this is an artifact or false positive from the data. Anecdotally, as I’ll argue, I think that many systematic macro funds really do execute strategies that are structurally biased toward being long bonds. But they also have a related bias. They like to own things where you collect a payment from someone else to own it. That someone else may be an issuer, a government or a hedger. Across macro funds, GTAA strategies and model portfolios, the only strategy that is as common as trend-following and a bond bias — systematic ones in particular — is buying higher yielding assets and selling lower yielding assets. We call these “carry trades”. Below are the significant betas (above and beyond the relationship to bonds) of each of the 74 funds to our measure of multi-asset carry trading. Most are positive, only a couple are negative, and the rest are largely positive but not significant after accounting for the existing carry component in their interest rate exposure.

Source: Epsilon Theory, HFR, eVestment April 2018. An investor cannot invest directly in an index. For illustrative purposes only.

Systematic Macro and Econometric GTAA funds have other systematic exposures as well, including a general bias toward short exposure in commodities, and a long bias toward equities, and these aren’t just present in the trend funds that have had those exposures because they have worked. They are common throughout.

If you talked about this with your tactical guy/gal or macro strategist, I know what he’s going to tell you, because it’s what they tell me, too. “There may be some of this, but we’re not directional. We may be long, we may be short, and we have a lot of other trades and signals.” I think you’ll find that this is sometimes completely true. For example, Bridgewater’s Pure Alpha strategy is consistently neutral to most market factors, although if you looked deeper into carry trades, you’d find a pretty persistent positioning in favor of higher yielding currencies. But generally speaking, your manager is telling you a half truth — literally. Across this universe, using static exposure to the most basic of market factors (stocks, bonds, commodities and currencies), you can explain around half of the variability in returns.  That’s not the problem, except that you’re paying them 1.5-and-20 for something you can and should get much cheaper. The problem is that after you take out the 50% you can explain with market factors, the hand-waving, black box, smoke-and-mirrors half they’re trying to sell you as their edge is a tire fire.

I mean, gods, look at this mess. Over the last five years, you would have gotten a positive Sharpe Ratio on whatever these guys did that wasn’t static long exposure to financial markets from only 23 of the 74 funds. Believe your model-driven macro guy when he tells you he isn’t only directionally long rates, -carry and trend-following. But be skeptical when he tells you that you ought to have a positive return expectation on whatever the other stuff he’s doing is.

Source: Epsilon Theory, HFR, eVestment April 2018. An investor cannot invest directly in an index. For illustrative purposes only.

The Many Moods of Macro

So how should we feel about the non-tire fire half of these returns — by which I mean the rates and carry half?

As I’ve alluded to above, the first mood of macro has always been betting on the behavior of central banks. There are a variety of reasons for that, but the first is that Systematic Macro, like other hedge fund strategies — and systematic ones in particular — is drawn to trades, strategies and markets with lower natural volatility. That means, generally, that positioning driven by the models will emphasize either directional exposure to lower volatility asset classes like bonds, or relative value trades (i.e. going long one asset and short a similar one). Importantly, it also means that the models will favor what they perceive as loosely correlated trades or positions. If you have better-than-random confidence in what central banks are likely to do, you have a full range of options to implement those views with characteristics that are attractive to a manager seeking to sell itself as limiting downside with significant uncorrelated upside. The other reason that funds were so fond of strategies reliant on their central bank behavioral models, of course, is that the models themselves were historically pretty effective.

But there’s another, more important reason for the attachment to central bank-driven econometric models. While most trades with significant upside potential and convexity require you to pay a premium — or be “short carry” — during the bond bull run of the last 30 years, a macro manager with a decent prediction model for the behaviors of central bank actions could put on trades with convex characteristics that paid him a positive carry over almost all of this three-decade span. For example, a prediction of a future rate cut would not only get the benefit of the signal but would also receive the term premium in an upward sloping rates curve, a premium that can be significant even in the front end of the curve.  Not only that, because central banks in different countries pursued frequently divergent policies with explicitly different inputs and aims, that manager could put on multiple such trades. And what’s more, these positions were uncorrelated to most portfolios’ primary sources of risk — we were living in a golden age!

This preference for long carry positioning is itself, I think, the second constant mood of macro. While it has historically manifested in part as a willingness to carry a directionally long bias in bonds, it also manifests in a preference for anything that pays you more to own it than something else. In Systematic Macro funds, you will see this in models which — from a variety of econometric inputs — ended up with a consistent preference for higher carry currencies, especially certain emerging markets currencies. The underlying model mechanics might differ. For example, the “emerging markets balance sheet quality” thinkpiece was a staple of the mid-2000s. The story went that the higher yields you earned for owning — I don’t know, Turkish lira — were compensation for perceived risk, but that the econometric support for fiscal stability and quality meant that the real risk of permanent capital loss was far lower. There are a hundred models with rationale like this that all lead to a pronounced bias toward carry.

The third ubiquitous mood of macro is pro-trend, and in particular, medium-to-long-term trend following. I’ve noted that much of the Systematic Macro universe overlaps with Managed Futures and CTAs that have made trend-following their bread and butter, but even among Econometric GTAA strategies, it is quite common to use trends and momentum to drive positions or to condition other models. It is, perhaps, even more common to measure trends in the econometric variables as strategies or factors of their own. Systematic models will also be driven toward pro-momentum stances by their risk management techniques. Because positive returning assets are generally lower volatility assets, things that have done well will tend to score well when the portfolios are being built, even if there is no explicit model saying, “Buy stuff that’s going up!” Beyond that, because many of these funds added implicit or explicit stop-loss logic in 2009, even among funds I would qualitatively (and subjectively) describe as following Econometric GTAA strategies, you can frequently explain much of the variation in returns with price momentum across asset classes of various horizons.

All of this is, incidentally, also why so many of your discretionary macro managers have spent the last two decades trotting out their Eurodollar guys to meet with you. They’re the guys who got paid to bet on central bank actions with diversifying, pro-momentum, carry-paying trades.

Systematic Macro in Three-Body Markets

Now, I don’t have much to say about whether I think that carry trades and momentum trades will work — or at least I don’t have much to say in this note. Suffice it to say that there are good behavioral and empirical reasons to think that they are persistent premia that compensate investors for bearing a certain type of risk, and also good reasons to think that may manifest somewhat differently in markets driven by Narrative abstractions. These are topics for another note.

But I do think it is clear that Econometric GTAA strategies have struggled mightily to adapt to a Narrative-driven market with everything else they are doing. When I have met with strategists and macro funds over the last few years, their language has been static. They walk me through the economic nonsense of negative interest rates and the upside asymmetry created by the zero barrier. They describe how relative growth rates, debt levels, deficits and trade balances cannot possibly support the relative yields of Treasuries and Bunds. They present compelling cases for curve trades between European markets that should converge given influences on ECB asset purchases, or for relative outperformance of this equity market over that because of the extension of corporate margins beyond some historical threshold for some historically long period of time. In other words, the Explicit Behavior models from the illustrated earlier in this note are running through the old motions on what central banks, asset owners and governments are going to do, and none of them are working.

It’s not as if managers and strategists haven’t tried to adapt to a world in which these trade drivers are subsumed into central bank communications strategy and the utilitization of markets by political figures. But when the models driving your trading are built to understand the cost and transmission mechanisms of capital, and your asset prices are driven by how investors think other investors are responding to a stronger adverb in front of a maybe hawkish adjective, it’s got to be more than just adapting and updating your models. It’s recognizing that over an expanded time horizon, asset prices are being driven by a wholly separate set of variables.

What worries me more than anything, however, is that the ability of GTAA and macro strategies to access the moods that may still work (mostly models that end up looking like carry trades) may be limited by their construction and their design in the emerging environment. As macro and GTAA strategists adapt to rising interest rates and inflation, I think you’re going to see some of their models under strain. They will be under strain because their central bank behavior models will be shouting “short rates”, but their DNA, their risk management framework, and maybe even explicit pro-carry models — will be screaming “God, that short looks expensive. Are you sure?” This dependency — implied already in the assessment of sensitivity to bond markets — is real. Since 2000, the average Global Macro hedge fund has generated roughly 2.4x its average return in months where the discount rate was reduced, with almost no advantage during periods with a rising discount rate. I think we may be entering an environment in which the only things that have really worked for these funds get lost in the wash. In their place, I think investors can expect to get weaker, lower Sharpe strategies based on Market Data-driven positioning. A lot of momentum and value, and not much novel insight — in many cases, a very expensive balanced market portfolio with a value overlay.

What do I do if I’m an allocator?

  • I don’t put all my trust in a macro strategist or tactical manager’s econometric models to ‘figure this all out’.
  • I probably own fewer of these funds and have less of my portfolio invested in them in the aggregate.
  • I ask for a manager’s rates and FX attribution. If it’s positive for most of the 2000s and starts to sour and turn over in, say, 2013-2014 and hasn’t recovered, I take the plastic binding rings out and file the pitchbook away in that special Iron Mountain filing cabinet in the print room. You know, the one with the lock on it that the guy comes for every couple weeks?
  • I challenge my macro PMs to explain how their models might approach rates and FX trading differently when rates are rising and if central bank policy remains largely coordinated. If they say, “We’re not directional and have always been agnostic on long or short positions, so it wouldn’t be any different,” I do my most exaggerated eye roll, put my chin on my fist, bat my eyelashes and give ‘em my best ca. 1991 Glamour-Shots-at-the-mall smile until they tell the truth.
  • I actively seek out managers who are incorporating and increasing the role of sentiment analysis, investor asset flows, market structure and Narrative, and reducing the role of econometric models, whether explicitly or through a systematic process for rotating capital between models (or turning off models whose alpha has evaporated).
  • I actively seek out managers who have actually figured out multiple robust models for trading commodities effectively other than trend-following models.
  • I continue to invest with managers accessing Systematic Macro’s traditional moods, but I’m only willing to pay what those replicable strategies are worth.
  • I look for managers who act boldly but hold their views loosely — in a word, humility.

The last goes for you and me, too. We must be humble about our ability to make good predictions about asset prices and returns. In a Three-Body Market, we should be even more humble than usual. But blindly handing over the reins of our asset allocation decisions to impressive people who claim to have developed the one model that will unravel this market’s mysteries is not an act of humility. It’s the very same act of expedience that caused so many of these managers to saddle themselves and their strategies to central banks, and it is the reason they’re feeling the spurs today. Buck it — or feel the spurs yourself.

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Investing with Icarus

The wind blows where it wishes, and you hear the sound of it, but cannot tell where it comes from and where it goes.

The Bible, John 3:8

As Narrative abstractions — cartoons — become our short-hand for things that used to have meaning, our models become more and more untethered from the reality they seek to reproduce. When wind becomes the thing-that-makes-the-leaves-move, then wind becomes a bear rubbing his back on the bark.

He that breaks a thing to find out what it is has left the path of wisdom.

The Lord of the Rings, J.R.R. Tolkien

Pursuing better returns by uncovering absolute truths about the companies and governments we invest in is not a serious enterprise in the face of markets rife with Narrative abstractions. It is a smiley-faced lie, a right-sounding idea that doesn’t work, and which we know doesn’t work. Selling the idea that it does to clients is the territory of the raccoon and the coyote. We can pursue it, or we can do the right things for ourselves and our clients. But not both.

Disneyland is presented as imaginary in order to make us believe that the rest is real, whereas all of Los Angeles and the America that surrounds it are no longer real, but belong to the hyperreal order and to the order of simulation. It is no longer a question of a false representation of reality (ideology) but of concealing the fact that the real is no longer real…

Simulacra and Simulation, Jean Baudrillard (1981)

How does Wall Street maintain the respectability of dishonest businesses? By declaring victory over straw men — active management is dead! Hedge funds lost the Buffett bet, beta won! Risk parity / vol-targeting / AI funds / quant funds are to blame! If you must sell that L.A. is real, you must create Disneyland.

“All right,” said Susan. “I’m not stupid. You’re saying humans need… fantasies to make life bearable.”

REALLY? AS IF IT WAS SOME KIND OF PINK PILL? NO. HUMANS NEED FANTASY TO BE HUMAN. TO BE THE PLACE WHERE THE FALLING ANGELS MEETS THE RISING APE.

“Tooth fairies? Hogfathers? Little—”

YES. AS PRACTICE. YOU HAVE TO START OUT LEARNING TO BELIEVE THE LITTLE LIES.

“So we can believe the big ones?”

YES. JUSTICE. MERCY. DUTY. THAT SORT OF THING.

“They’re not the same at all!”

YOU THINK SO? THEN TAKE THE UNIVERSE AND GRIND IT DOWN TO THE FINEST POWDER AND SIEVE IT THROUGH THE FINEST SIEVE AND THEN SHOW ME ONE ATOM OF JUSTICE, ONE MOLECULE OF MERCY. AND YET — Death waved a hand. AND YET YOU ACT AS IF THERE IS SOME IDEAL ORDER IN THE WORLD, AS IF THERE IS SOME…SOME RIGHTNESS IN THE UNIVERSE BY WHICH IT MAY BE JUDGED.

“Yes, but people have got to believe that, or what’s the point—”

MY POINT EXACTLY.

Hogfather, Terry Pratchett (1997)

So long as the government requires financial markets to act as a utility, and so long as it makes more sense for big tech companies to hire evangelists than CEOs — until the farmer comes out with his gun – we have only a few choices:

  1. We can be raccoons: We can recognize the overwhelming influence of abstractions and continue to sell products and ideas that don’t.
  2. We can be coyotes: We can recognize the overwhelming influence of abstractions and DESIGN new products and ideas that don’t.
  3. We can be victims: We can let the raccoons and coyotes run rampant over the farm.
  4. We can insulate: We can push back from the table and try to do the things that aren’t abstractions. Real things. Physical things. Things that put spendable currency in our accounts.
  5. We can engage: We can do our best to think about how to change our investment strategies and processes to respond to abstraction-driven markets.

These aren’t mutually exclusive, although only two are worthwhile. Ben’s DNA is long vol, so he wrote about how to insulate. My DNA is short vol. This note is first in a series on how to engage.

Speaking of DNA, there are few fields of study I find as thrilling as the intersection of anthropology and genetic geneaology. What I mean by that is how people lived, died and moved, and how their cultures and lineages moved with them. Yes, if kicking off notes with the old King James didn’t give you enough of a hint, I’m a big hit at parties.

Some of the appeal of genetic anthropology comes from the simple pleasures it offers, like the satisfaction of watching white supremacist idiots discover that they are mutts just like the rest of us.

The second appeal is the grand scale of ancestry and human movement, even over cosmologically infintestimal periods of time. This appeal is timeless. For example, in a legend common to three of the world’s great religions, God promises to multiply Abraham’s descendants as the stars of the heaven and as the sand on the seashore. It’s a pretty attractive promise, but temper your excitement — it was a reward for being a hair’s breadth away from murdering his son. The promises are poetic, of course, but the scope of the two is surprisingly different.

There are somewhere around 100 billion to one trillion stars in the Milky Way, an estimate which would vary based on how you estimated the galaxy’s total mass through the gravity it exerted and based on what you assumed was the average type of star. We’ve discovered a Wolf-Rayet star in the Magellanic Cloud with mass perhaps 300 times that of our sun, for example. It is so much larger than our sun that its surface would reach almost a third of the current distance to Mercury. Icarus wouldn’t stand a chance. On the other hand, we’ve discovered a red dwarf only 19 light-years away with less than 10% of the mass of the sun. But the 100 billion to one trillion range is a fair estimate. Earth has already seen 100 billion human lives. It will (hopefully) see its trillionth at some point between the year 2500 and 3000, if y’all could stop killing each other. Still, if you’re willing to ignore that we can see stars from other galaxies, too, I think we can prematurely give this one to Abraham.

As for the sand, there are about seven or eight quintillion grains on the earth. There’s just no way, even if Elon manages to get us off this planet before the next mass extinction event.

Interestingly, if you look backward, that isn’t quite true. When it comes to lineage, exponential math doesn’t always work going forward. One couple dies without any offspring, while another has a dozen children. But it always works going backward. Everyone has two parents and four grandparents. Based on most of those traditions holding that Abraham lived around 2,000 BC, we can estimate that the average living person has about 1.5 quindecillion ancestors from that time. Given that there were only about 72 million people alive at the time, that means that each of those individuals, on average, shows up in your family tree about 20 duodecillion times. That’s a 20 with 39 zeros. Congratulations! Math is amazing, and you are inbred.

The third appeal is that the really interesting findings are new. Very new. Anthropologists, of course, have theorized about the propagation and spread of cultures through comparative review of ancient art, tools, jewelry, burial sites and artifacts for centuries. Linguists can lean on anthropological techniques, but can also compare similar or derived grammar, vocabulary, and the like to identify how languages originated and spread. Maybe even some sense of where they came from. DNA has been used to develop and cultivate theories about human migrations and the spread of cultures for a shorter time, but in earnest starting in the late 1990s into the early 2000s. These studies have principally relied on the DNA of living individuals. Scientists examine current populations and theorize how ancient populations would have had to migrate to create the current distribution of various genetic admixtures — archetypes of varying compositions that can be generalized, like “Near Eastern Farmers.”

But in the last five years, the real excitement has been in the enrichment and analysis of ancient DNA. That means that, instead of just looking at modern populations and developing models to predict how they may have gotten there, we instead may look at the actual DNA of people who lived and died in some place in the distant past. We don’t have to guess how people moved and where they came from based on second-hand sources, like the DNA of people living in the same place thousands of years later, or on the pottery that they left behind.

We can know the truth.

Desperate for Wind

The allure of a fundamental truth is powerful. It’s the draw of science, and it’s a good thing. Understanding the true physical properties of materials and substances, for example, is the foundation of just about every good thing in our world. I mean, except for justice, mercy, duty, that sort of thing. We have the food we eat because those who went before discovered human chemical and enzymatic processes for digestion, and learned the mineral, chemical, water and solar needs for the plants that would be digestible. We have the devices we carry in our pockets because many thousands of researchers, designers and other scientists discovered the electrical conductivity of copper, the thermal conductivity of aluminum, the fracture toughness of various types of glass and a million other things.

I grew up around this kind of thinking. My dad worked for the Dow Chemical Company for some 40 years. Most of that time he spent as a maintenance engineer, an expert in predicting and accounting for the potential failure of devices and equipment used in the production (mostly) of polyethylene. His professional life’s work was perfecting the process of root-cause analysis. There may not be anyone in the world who knows more about how and why a furnace in a light hydrocarbons facility might fail. It may sound hyperspecialized, but that kind of laser-focused search for truth is something I took and take a lot of pride in.

Investors are hungry for that kind of clarity about markets. But it doesn’t exist. In The Myth of Market In-Itself, I wrote about investors’ vain obsession with finding root causes in media, economic news and Ks and Qs. Ben recently wrote about it pseudo-pseudonymously as Neb Tnuh, mourning the conversion of Real Things into cartoons, crude abstractions that investors are forced to treat like the authentic article:

Do I invest on the basis of reality, meaning the fact that wage inflation is, in fact, picking up in a remarkably steady fashion in the real economy? Or do I invest on the basis of Narrative abstractions that I can anticipate being presented and represented to markets at regularly scheduled moments of theater? Because the investment strategy for the one is almost diametrically opposed to the investment strategy for the other.

Like many Epsilon Theory readers, I am Neb Tnuh. Like Neb, I want to evaluate businesses and governments again. I want to understand their business models, evaluate their prospects against their competitors and subtitutes, quantify the return I can expect and the return I ought to demand for the risk, and seek out investment opportunities where the former exceeds the latter. I want this. But like everything else in life, wanting something to be possible doesn’t make it so.

It also doesn’t make it noble. Arch-raccoon James Altucher fancies himself Neb Tnuh, too:

“But business is just a vehicle for transforming the ideas in your head into something real, something tangible, that actually improves the lives of others. To create something unique and beautiful and valuable is very hard. It’s very special to do. It doesn’t happen fast.”
― James Altucher

And sure, there are ways to pull away from the table. There are ways to be short abstractions, like Neb recommends. Before he wrote The Icarus Moment, he wrote Hobson’s Choice, which described some of the few ways that all the Neb Tnuhs out there can reject the false choice between investing on the basis of a reality that is decoupled from risk and return, or not investing at all. These are strategies to insulate against Narrative abstractions, and I think they should be larger parts of almost every investor’s portfolio. Am I being explicit and actionable enough here? I’m talking about more real assets.

But a strategy which only insulates isn’t practical. It’s not practical for asset owners with boards, or actuarial returns, or a need to hit traditional benchmarks. It’s not practical for individuals who may not have the luxury, wealth or flexibility to, oh, I don’t know, buy an airport or 3,000 acres of northern red oak forests in Georgia. It probably isn’t desirable either. First, that level of underdiversification implies an extreme difference in return expectation, and I’m not going to leave that free lunch on the table. Neither should you. Second, the raison d’etre of turning the market into a utility, of propagating central bank missionaries and evangelist CEOs is the belief that those behaviors are at least somewhat predictable. If we’re not applying that in some measure to the rest of our portfolios, we’ve probably left something else on the table.

And so, unless we would be victims of the coyotes and raccoons who would sell us their own panaceas to this investing environment, we must engage with Narrative-driven markets. But it is hard. It is hard because the nature of abstractions is to require far more information — which usually means more time, too — to change their state. Think about when you’re explaining some complicated analogy to someone and they get confused (did you like my meta joke?). How much longer does it take you to get your conversation back on track? Think about the Keynsian Newspaper Beauty Contest. When you’re playing at the third or fourth level, how much more difficult is it to hold the pattern of what you’re evaluating in your mind, and how much more difficult is it to change that pattern to respond to new information once you’ve approximated it with some other thing, some heuristic or placeholder?

When an asset’s price, volatility behavior or direction is being driven by agreed-upon abstractions, so too is the required information to change its state far greater than usual. Missionaries explain away bad news, or create a new pro forma metric. Media members promote the new spin on the story. Supplicants call on confirmation bias to interpret it based on their existing thesis. And the contrarians who could move the price have all gone to the Hamptons for the decade. Notice how volatility spikes briefly and then disappears?

The question on whether to engage, or to try your luck with strategies that presume a strong, efficient link between economic facts and asset prices, is a question of timing. Unless your investment horizon — by which I mean the horizon over which your trade can go profoundly against you without your getting fired (if you’re a professional) or changing your mind (whether or not you’re a professional) — is more than 10 years, I simply don’t think you can have any confidence that your fundamental analysis has anything more than even odds. Sorry. And in case you were wondering, the answer is no. I don’t care who you are. You do not have a 10+ year horizon to survive being told by Mr. Market you were wrong without being fired or putting yourself under extreme pressure to change your mind.

Investing in a Time of Icarus

But we have already written about a lot of this. You know that Ben and I have said that many of these strategies just aren’t going to work the way that they used to, or when we’re looking for low-hanging fruit, that they haven’t worked the way that we all expected them to. You know that we think this is largely the result of markets and economies becoming utilities, Narrative replacing economic sensibility, and governments and oligarchs stepping into their own as missionaries for that utility and the Narratives that support it.

But what do we do? What do we do differently?

I’ve written about part of the answer fairly plainly in the Things that Matter and the Things that Don’t Matter series from 2017. There is a finite, definable list of investment principles which matter all the time, even in an Icarus Moment. Ben has written about the second element, which is to insulate.

For those who want to engage and continue the search for alpha, the answer depends.

First, it depends on the definition of alpha. When I say alpha, I mean any asset class-level decision that causes a portfolio to deviate from either the most diversified possible portfolio or a market cap-weighted portfolio of all global financial assets. I also mean any security-level decision that causes a portfolio to deviate from the broadest possible market-cap weighted benchmark for that asset class. It’s a simple definition that doesn’t get pedantic about whether a systematic active strategy is really a kind of “beta.” Sure it is. Or no, it’s not. It’s a stupid debate. I don’t care.

Second, it depends on the type of investment strategy you are using. It also depends on your methodology for implementing that strategy. Incorporating both of these requires some kind of framework to discuss.

Here’s what we’ll do: the dimensions I will use for the framework will be different from style boxes, and they’ll be different from categories used by many hedge fund index providers or asset allocators. I will define the categories instead by how I think they interact with an Icarus Moment, or a Three-Body Market — with a market in which asset price movements are heavily influenced by Narratives over an extended period of time.

The first dimension of those categories is what basis on which the strategy seeks to predict future asset prices (by which I include relative future asset prices). I roughly split strategy types into three categories: Economic Models, Behavioral Models and Idiosyncratic Models. Economic Models, in my definition, seek to predict future asset prices principally on the basis of actual and projected economic data about an economy, a financial market or an issuer, whether it’s a company or a government. Behavioral Models may incorporate some elements of Economic Models, but are principally driven by suppositions and beliefs about the behaviors of other market participants rather than the underlying companies. Idiosyncratic Models include various strategies which may even seek to exert direct influence on the future price of an asset.

For the second dimension of the framework, I think it is useful to separate investment strategies which are Systematic from those which are Discretionary. By Systematic Strategies, I refer to alpha-seeking strategies that reflect more-or-less static, if potentially emergent, beliefs about how prices are determined by certain characteristics or states, and whether those characteristics or states are directly related to economic data or more clearly influenced by observable investor behaviors.  The second category, Discretionary Strategies, refers to those in which there may be a process associated with similar beliefs, but in which the decision is made based on the judgment of a human portfolio manager. There are frequently observer effects in any investment strategy (i.e. where the act of observing something changes it), but particularly so in Narrative-driven markets. The systematic/discretionary dimension is important to understanding how this can manifest.

Those two dimensions give us six broad categories, which I have filled in with general descriptions of strategies that I think fall into each. There are things I haven’t captured here, but not many. Of active traditional and non-traditional investment strategies in public markets, I’m comfortable that this captures more than 80%. Close enough for government work.

Over the next few months, I will write a piece covering each of these six categories. My aim with this exercise is three-fold. For those who elect to both insulate and engage:

  • I want to tell you the strategies that I don’t think will work.
  • I want to tell allocators / asset owners how I think the evaluation of the strategies that may work should change.
  • I want to tell asset managers how I think they should consider adapting their strategies so that they still work in this environment.

If you think that I have bad news for the strategies on the left third of the table, thank you for paying attention. If you’re looking for a prize at the bottom, there is none.

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The Three-Body Portfolio

Bernard: If knowledge isn’t self-knowledge, it isn’t doing much, mate. Is the universe expanding? Is it contracting? Is it standing on one leg and singing ‘When Father Painted the Parlour’? Leave me out. I can expand my universe without you.

‘She walks in beauty, like the night of cloudless climes and starry skies, and all that’s best of dark and bright meet in her aspect and her eyes.”

  Arcadia, Tom Stoppard

It is a romantic thought, that we might divorce our personal universe from the universe around us. For us investors, maybe that means to hide in a room building an elegant model to work out the true value of a thing. I mean, by itself it’s a complete waste of time…but so romantic!

To make a prairie it takes a clover and one bee,
One clover, and a bee.
And revery.
The revery alone will do,
If bees are few.
To make a prairie”, Emily Dickinson

Since I’m bogarting Ben’s title, I might as well steal his best literary reference, too. The market isn’t necessarily tied to ‘fundamentals’ any more than a prairie is to bees. Revery alone will do, and sometimes it will do for a very, very, very long time.

A true German can’t abide the French,
But he’ll gladly drink their wine.
— Faust, Johann Wolfgang von Goethe

You don’t have to be French to drink their wine, y’all. Being part of the Epsilon Theory pack doesn’t mean buying into narratives. It means understanding that in a market, if it matters to someone, it should matter to everyone. And narratives matter to a whole lot of someones.


Epsilon Theory started from a pretty simple idea. Ben observed that no financial or econometric model can ever fully explain the returns or volatility of financial markets. I don’t think he’ll be too mad if I point out that this wasn’t a particularly novel observation. After all, every statistical model in the world has an error term that basically accounts for this — epsilon.

β + α + ε

Said less vaguely, epsilon is the way in which — as people — investors respond to both financial and non-financial stimuli in various non-random ways. It is an observation that a not insignificant portion of the systematic (i.e. not diversifiable) risk and return in your portfolio is completely divorced from the risks faced by economies and businesses. It is a feature only of the markets and the people who comprise them[1].

Some regard changing perceptions, sentiment and shifting narratives as a source of short-term volatility in securities prices, and little more. Indeed, that is the implication of the old Benjamin Graham trope I disputed in The Myth of Market In-Itself — that the market is a short-run voting machine, but a long-run weighing machine. Sure, sentiment may matter in the short run, but eventually truth will out! I did a fair job, I think, of identifying my issues with that point of view, but as per usual, it was Ben that really got to the heart of the issue. In his latest note, he characterizes the occasional sharp rise in unpredictability of market outcomes — not to be mistaken for volatility — as the result of a Three-Body Problem. You do yourself a disservice if you haven’t read the piece, and probably a greater disservice if you haven’t read the Liu Cixin book of the same title recommended in it. But in short, a three-body problem refers to a system that is solvable not through elegant algorithm, but only through brute-force computation. There is no closed-form solution to predict the future locations of a set of three planetary bodies in a vacuum like, say, the Earth, the sun and the moon.

In most environments, where the purpose of markets is to efficiently and accurately price risk of various uses of capital, those markets tend to behave more or less like two-body systems. The interaction of Planet A (which we’ll call ‘fundamental data’) and Planet B (which we’ll call ‘prices’) is generally predictable. Oh sure, there’s volatility. Remember, not everyone agrees on the starting point and velocity of Planet A — at least, not since Reg FD, anyway. Information takes time to propagate. But we also know that Planet C (let’s call it ‘epsilon’) is sitting out there somewhere. Yet it’s far enough away that its gravity can’t do more than induce short- and medium term distortions in the relationship between A and B. If you knew the truth about the starting positions and velocities of Planet A and Planet B, however, you could develop a formula that would tell you within a pretty fair margin where prices would be down the line.

In this typical state of the world, being a better investor has meant getting better at uncovering the truth about Planet A so that you can predict Planet B’s future location. It’s no wonder that a generation of investors grew up learning about traditional security analysis, the only way investment management is taught in every business school in the world.

Still, everyone from the most well-respected market commentators to the staunchest Graham and Dodd-quoting undergrad recognizes the existence of markets in which Planet C — epsilon — contributes its gravity to the system. Among those periods in which our ability to make predictions on the basis of relationships between fundamental data and securities prices is especially poor, are those we know as bubbles and manias. William Bernstein characterizes these periods as those typified by the “flood of new investors who swallow plausible stories in place of doing the hard math.” He goes on to quote Templeton, admonishing investors, “The four most expensive words in the English language are ‘This time it’s different.’”

Well, guess what? Roll your eyes at the expression to your heart’s content, but I’m telling you what Ben has been telling you for years now:

This Time It’s Different.

It’s not different because people really got it right this time (in ways they missed every other time) about some new technology that’s going to Change The World! Electric cars, cryptocurrency, AI and automation, these may all be fabulous things, and they may well prove to be game-changers for productivity and returns on capital down the line, but if you think any of those things explain current valuations, you’re nuts. You’re also wrong.

It’s different because financial markets are no longer a mechanism for price discovery and the pricing of risk of capital allocation decisions.

Markets have been made into a utility. More to the point, they have been made into a political utility, a tool for ensuring wealth and stability of our political structures. The easing tools we dabbled in to stabilize prior business cycles were brought to bear instead as tools for propping up and expanding financial asset prices. Beyond the direct marginal price impact of the easing itself, central bankers tailored communications policies to create Pavlovian responses to every narrative. Our President tweets about the policy implications when the S&P 500 hits new highs, for God’s sake[2]. This isn’t a secret, y’all. The singular intent of every central banker in the world is to keep the prices of financial assets from going down, and the singular intent of every government that puts those central bankers in power is to ensure that they do so, in order to retain social stability. Sure, there’s a dual mandate. But the mandates aren’t employment and price stability. They’re (1) expanding financial asset prices and (2) effectively marketing the idea of corresponding wealth effects to the public.

Markets have also rapidly become a social utility, an inextricable part of every contract between governments and the governed. Underfunded pensions and undersized boomer 401(k) accounts mean that ownership of risky assets is not a choice driven by diversification or relative return expectations, but by the fact that it is the only asset they can buy that has any potential of meeting the returns they would need to be adequately funded. Let’s say that you are running a state pension plan that is 65% funded. Your legislature is telling you that no help is coming from the state budget. You and every member of your agency will be fired if you even suggest cutting benefits, if you even have that authority. Your consultant or internal staff just did their new mean reversion-based capital markets return projections, and higher valuations mean projected returns on everything are lower. What’s worse, your funded status assumes returns that are higher than anything on their sheet. You are being presented with a Hobson’s Choice — behind Door #1, you get fired, and behind Door #2, you lever up your stock exposure with an increased private equity allocation. This a brutal position to be in.

And sure, like most markets with bubble-like characteristics, this one has become a utility for psychic value as well. Investors buy Bitcoin on the narrative-driven belief that it is an ‘investment’ in the technology, a way to participate in shifting the economy toward privately negotiated and settled transactions. It isn’t. We’ve all seen the absurd stock charts of companies who did nothing more than add “blockchain” to their names. We’ve observed TSLA, NFLX and CRM continue to trade on earnings reports that provide zero incremental data on business direction or momentum but heroic narratives that the sell side dutifully push out to the masses looking for good stories. If you must own risky assets and those assets don’t have growth, then revery alone will do, if bees are few.

It may comfort us to say that “The market has been divorced from fundamentals for so long, but eventually it must swing back.” And it will. The point of this note, and the point of The Three Body-Problem isn’t to say that it won’t. Planet C will drift away again, and outcomes will look more like what economic and business fundamentals would predict. More like our historical analysis of what drives good, high quality investments. But too many investors are comforting themselves with the stories of the 1990s, of the Nifty Fifty, and the idea that non-fundamentally-driven markets mean return to sanity after five to ten years. But they don’t have to. And because of the utilitization of markets, because of the exit of passive-oriented investors from the price-setting margin of markets, it’s possible that they won’t for a very long time.

This Three-Body Problem isn’t going anywhere for a while.

The Three-Body Portfolio

When I began this Code series at the beginning of 2017, I kicked it off with A Man Must Have a Code, a conversation about why we think that all investors ought to have a consistent way of approaching their major investment decisions. I posited that a code ought to consist of a concise list of Things that Matter, Things that Don’t Matter and Things that Don’t Always Matter (But Do Now). And so my notes have focused on investing principles that I think of as generalized solutions. These are things that I believe are true in both Two-Body and Three-Body Markets:

  1. In I am Spartacus, I wrote that the passive-active debate doesn’t matter, and that the premise itself is fraudulent.
  2. In What a Good-Looking Question, I wrote that trying to pick stocks doesn’t matter, and is largely a waste of time for the majority of investors.
  3. In Break the Wheel, I argued that fund picking doesn’t matter either, and took on the cyclical, mean-reverting patterns by which we evaluate fund managers.
  4. And They Did Live by Watchfires highlighted how whatever skill we think we have in timing and trading (which is probably none) doesn’t matter anyway.
  5. In Chili P is My Signature, I wrote that the typical half-hearted tilts, even to legitimate factors like value and momentum, don’t matter either.
  6. In Whom Fortune Favors (Part 2 here), I wrote that quantity of risk matters more than anything else (and that most investors probably aren’t taking enough).
  7. In You Still Have Made a Choice, I wrote that maximizing the benefits of diversification matters more than the vast majority of views we may have on one market over another.
  8. In The Myth of Market In-Itself (Part 2 here), I wrote that investor behavior matters, and spent a lot of electrons on the idea that returns are always a reflection of human behavior and emotion.
  9. In Wall Street’s Merry Pranks, I acknowledged that costs matter, but emphasized that trading costs, taxes and indirect costs from bad buy/sell behaviors nearly always matter more than the far more frequently maligned advisory and fund management expenses.

In all of these, you’ve gotten a healthy dose of emphasis on getting beta — how we get exposure to financial markets — right. But you’ve seen precious little on alpha — uncorrelated sources of non-systematic, incremental return. Where we dealt with the usual ways in which investors seek out alpha, I have been critical. Maybe even derisive. Sorry, not sorry. There’s a reason:

Even in normal environments, alpha is hard.

Alpha is hard because it’s hard to measure. It’s hard to know if what we’re doing is actually something that adds value, or if we’re being fooled by randomness. We may even just be layering on some other systematic factor, or beta, that is just compensating us for taking additional risk. Every couple of years, someone rediscovers that bond managers tend to “have more persistent alpha”, and a couple of weeks later, someone rediscovers that bond managers just layer on more corporate credit risk than the benchmark. Every couple years, stock-picking strategies go back into vogue — it’s a stock-pickers’ market, they say! Fundamentals matter again! No, they don’t. You’re structurally smaller cap and loaded up on higher volatility names, and when those factors work you feel smart.

Alpha is hard because randomness is an insanely powerful force in the universe and within our industry. Any time I hear a fund manager or financial advisor say, “Look, a 10-year track record like that doesn’t just happen by accident,” every part of me wants to scream, “Yes, it bloody well does, and if there’s anything true and good about mathematics at all, it will happen by accident ALL THE TIME.” I’m not saying that Warren Buffett’s success is an accident, but I am absolutely saying that with as many investors as there are, it is improbable that history would not create Warren Buffett’s track record. We are all looking for a way to increase returns that doesn’t involve taking more risk, and once we’ve gotten all we can out of diversification, believing in alpha is our only choice.

In a three-body market where epsilon exerts its gravity, alpha is not just hard. Finding it emphasizes entirely different types of data and analysis. The whole point of recognizing an increasingly chaotic system is that our confidence in the causal relationships over time and between assets at a point in time drops significantly. Given that tools relying on temporal and cross-asset relationships are the ones we most often use to evaluate investment strategies, it puts us in a bit of a pickle! Sure, there are some exogenous strategies like high-frequency trading that are pretty effective across environments for mechanical reasons. But many of the “time-tested” strategies that work in Two-Body Markets can stop working for a very long time, and the “new” strategies we identify in our in-sample periods can start looking like hot garbage the second we drop them out-of-sample. The search for alpha in this kind of environment — even when it occasionally bears fruit — is time-consuming, expensive, and often leads to unintended risk, cost and diversification decisions that more than offset any positive that they generate. We get a stock pick right, but it is dwarfed by sector effects. We pick the right country to invest in, but get killed on currency. For many investors – for most investors — this means that trying to beat “the market” on an intra-asset class level or at on an inter-asset class level through tactical asset allocation should not be part of their playbook. For these people, I hope the Code to this point is a useful tool. Thanks for reading.

But for those who know they won’t be content with that very adequate outcome, I’ll do my best to talk about alpha strategies that I think can work in a Three-Body Market. That means identifying the likelihood of success of various analytical security and asset class selection strategies. It also means giving you my perspective on what edge (ugh, I know) would even conceivably look like in a fund manager. As we close the door on 2017, I also close my series on The Things That Matter and The Things that Don’t Matter. As we open the door on 2018, look forward to a new series on the Things that Don’t Always Matter (But Do Now).

Together, I think they’ll provide a pretty good roadmap for the Three-Body Portfolio. But I mean, even if they don’t, at worst you’ll get to read horror stories of terrible fund managers, so what have you really got to lose?

From Texas, my best wishes for a prosperous New Year to you and yours.

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[1] I know some will pipe in that this is just part of beta. This is mathematically true in that systematic sources of risk are what we mean when we say ‘betas’, but anyone reading this who pretends not to understand that what we’re talking about is a specific component of systematic risk that tends to be understated in favor of econometric and financial variables is a filthy pedant.

[2] Actually, he tweets about the Dow hitting new highs, because of course he refers to the Dow.