Looking for Laffer-Likes


Ed Meese jumped in, as he usually did when he saw that his boss was discomfited and at a loss.

“What about the revenue feedback from the tax bill” he asked. “You haven’t taken account of that in these scary numbers.” His tone was slightly annoyed.

Meese was referring, of course, to the Laffer curve. The whole California gang had taken it literally (and primitively). The way they talked, they seemed to expect that once the supply-side tax cut was in effect, additional revenue would start to fall, manna-like, from the heavens.

The Triumph of Politics, by David Stockman

Some years ago, back when Republican, conservative and Reaganite were circles in a Venn diagram that actually overlapped, I considered myself all three. And so, the chart above is familiar to me. It probably is to you, too.

Now, I’ve never cared all that much about tax receipts. I am almost always in favor of lower taxes because I believe that the freedom to decide what we do with our money is among our most powerful and important freedoms. I also believe that the more money the state has, the stronger its belief that it has a mandate to control and direct economic activity, things at which it is especially lousy.

But there really was a cadre of politicians and thinkers who believed in the Laffer Curve. Like, really believed. Literally believed. It was still fondly preached in some business school courses into the 2000s, I can attest, and maybe still is today. Its adherents believed – very much in earnest – that if we reduced tax rates, not only would it lead to greater economic activity, but to higher tax revenue as a result of that activity. This is…well, it’s not true. I believe that the first derivative of it is true, especially at higher marginal rates like we experienced prior to 1981, and certainly prior to 1964. And for freedom- and efficiency-minded people, that’s what probably matters anyway. But that wasn’t the argument. There were people for whom the expectation of increased receipts was a legitimate belief and a reason for lowering taxes.

Why? Because it was a story they wanted to believe anyway? Yes, yes, sure.

But more importantly, because it provided what looked like the safe harbor of fact within a foggy sea of complexity.

Predicting how changes to tax policy will influence economic activity and tax receipts is extremely messy work. For better or worse, the Laffer curve gives us an anchor. We know that a 0% tax rate will not yield tax revenue. We also know that a 100% tax rate – barring enslavement, corruption or criminality – will yield effectively zero revenue. Our instinct when we lack a good prediction framework (whether a systematic or discretionary mental model) will be to lean on the rare bits we do understand, like tax behaviors at extreme levels, to help us make predictions about what happens at the margin.

And in all sorts of cases like this, this temptation to head in the direction of our safe harbors leads us astray. These are the Laffer-Likes.

Some may immediately spring to your mind. But as we turn the page on 2018, there are two Laffer-Likes that are front-and-center in a lot of investors’ minds and in the narratives of the media, the sell side and the buy side: (1) the increasing passive share of financial markets and (2) the prevalence of algorithmic trading.

We’ve waded into the debate on active vs. passive investment management before, mostly by calling it a stupid debate that isn’t really about the thing that people say it’s about. Our view hasn’t changed, so I won’t add any wood to that fire. No, the Laffer-Like here is about what happens to financial markets and asset prices when the market becomes increasingly passive. How is this changing price discovery? Does it change how active strategies will work? Does it break traditional mean-reverting patterns in the dominance of traditional styles like value and growth? Will it create long periods of low volatility followed by bouts of extreme volatility? Will it create bad behaviors by management teams?

These are all fair questions. Good questions. Questions investors ought to be asking.

They are also nearly impossible questions to answer definitively. We will be tempted – and it will feel very reasonable to us – to consider what we can know. One of the few things we can truly know is that a market that is fully passive is not a market in any sense. It isn’t active in setting prices, testing prices or responding to information. When the market becomes more passive, it should be no surprise, then, that we see these descriptions in active fund manager quarterly newsletters or annual outlook pieces: “Passive investing is creating a pro-momentum market that doesn’t process information about companies!”

It is a statement of indisputable fact when uttered about a market of extremes in passive management. It is also a statement of very limited utility when applied to most other circumstances. The truth? Despite the increase in passive management, I would very comfortably contending that the amount of information influencing asset prices is still as broad and deep as it has ever been. That information may not all be the company-level fundamental information self-designated ‘real’ investors would prefer, but for better or worse, we are a world awash in information that influences the brains and behaviors of active investment decision-makers. Said another way, we are a long way off from passive extremes and the broken dynamics they would bring about in markets.  

What should we be thinking about instead? Think about what classes of investors have been more or less likely to move to passive strategies. Think about how that may have influenced the incentives, objectives and behavioral makeup of both of those universes, and the behaviors you would expect to be more or less prevalent among active investors as a result. Think about how that might influence broad market shifting behaviors (e.g. moving to cash) in certain market events. In other words, stop with the “passive investing means that information isn’t what moves asset prices” copouts, and start thinking about why the information that moves asset prices is different, and why the composition of the people responding to that information is different.

Much of the same could be said for the other Laffer-Like bogeyman of the day, the dreaded algorithm, which in modern usage is just a fancy word for, “anything that causes price activity that isn’t what I think it should be based on the finite number of things I care about and monitor in my own investment activities.” We know a lot about the effects of the extreme straw men for “algos” that we create, and extrapolate that to represent behaviors at the margins. It is perilous, but also deserves more than an In Brief mention. We will be talking much more about this one in 2019.

In the meantime, as we go through our end-of-year / beginning-of-year thought experiments and considerations, we might spare a moment to think about our own Laffer-Likes – the complex systems about which our jobs require us to develop a view, and about which our only firm foundations live in the extremes, and not at the margin of our work.

It is among the most common ways that we delude ourselves by draping our predispositions in non-explanatory facts. It is also among the ways in which we make ourselves vulnerable to right-sounding stories and narratives.


Building the Narrative Machine


This week, inspired by Ben’s note We’re Doing It Wrong, I thought that I would write a follow up note on ‘Eight learnings on incorporating Narrative observations into a system’.

This note is not written to be deeply technical, nor is it written from an envelope-pushing theoretical perspective. Rather it is from the perspective of a practitioner and therefore from a practical – and hopefully digestible – point of view on how to use visualizations of Narrative-space in real-world decisions and analysis.

By way of background, I’ve stared at and tried to make actionable sense of many, many thousands of machine-generated (or semi-machine generated) narrative analysis over the years, in areas ranging from political campaigns to military intelligence to corporate disaster response to equities trading systems.

And yet I feel like I’ve only touched the very surface of the power of this approach and what can be done. There is so much work to do in this area, so much potential, and while the Narrative is as probably old as language itself (probably older), it feels more relevant that ever right now to be able to observe, understand and incorporate the Narrative into our systems for making sense of the world.

Couple of framing comments before we get going with ‘the learnings’:

  • I’m assuming that most Epsilon Theory readers are not deeply technically engaged in Machine Learning so I’ve used terms such as ‘vector’ and ‘dimensionality reduction’ in somewhat liberal, expressionist ways that I think are intuitive and most usefully convey core concepts, rather than in precise, formal ways (and stayed away from terms like ‘centrality’ and ‘vertices’ and other less intuitive but more precise graph theory terminology).
  • If you are interested in exploring some of the concepts mentioned below in more depth and with more formality, I would suggest starting by working your way through the book Python Machine Learning by Sebastian Raschka. It is a good, foundational blend of theory and practical application of Machine Learning and requires no prior knowledge of, or experience in, coding (of course, there are many other good resources on this topic too, I just happen to be familiar with and like this one).
  • For full transparency, I was previously the CEO of Quid, a pioneer in Natural Language Processing (NLP) and Graph Theory that generates many of the Narrative maps seen on Epsilon Theory. This note is not Quid-specific, but rather takes on the overall topic of machine-assisted Narrative observation. Some of the examples below can be very well executed on Quid, some can be performed using free open source software (with some configuration work), some require custom extensions of both / either.

Learning #1: It’s not unstructured data, it’s really complex structured data

Firstly, and foundationally, to state the obvious, the Narrative is most often expressed in language (sometimes in pictures, dance and other forms, too, but for today let us stick to language).

Oftentimes language is referred to as ‘unstructured data’, which is database data model terminology.

However, ‘unstructured data’ has gradually crept into general parlance and led to this kind of implicit, half-formed notion that the Narrative is therefore intractable except in a thin, reductionist way.

This is a mistake.

Clearly we know that language itself is not unstructured, otherwise you could not read this; it just has a high degree of complexity in its structure.

This notion of language and the Narrative being intractable except in a thin, reductionist way is the first mental block to rid ourselves of.

We will come back to this point, but this is why using high dimensional graphs is a useful technique for analyzing the Narrative and why squishing the dimensionality way down into a ‘factor’ to put alongside categorical and numerical variables in a regression-type approach is typically pretty unrewarding.

Learning #2: Narrative ≠ Sentiment

The only word that is more irritatingly misused than ‘factor’ when talking about Narrative is the ‘S word’: Sentiment.

Sentiment is to NLP as sushi is to Japanese food. It’s fine, it’s in the set, but is very far from the whole cuisine.

And most sentiment analyzers are like the $10 takeout lunch special with green-dyed horseradish instead of wasabi – it’s not capturing the right flavor. At all. For example, imagine you are trying to evaluate the sentiment of equity analyst reports, where the word ‘overweight’ is a positive thing, but you have trained your sentiment analyzer on general data (such New York Times articles), where ‘overweight’ is part of the same negative vector as ‘morbid obesity’. Your sentiment analysis isn’t going to be just in error, it’s going to be perversely in error.

Meanwhile we have very good okonomiyaki, yakitori, tempura, onigiri, sukiyaki and hundreds of other kinds of refined deliciousness we are ignoring.

Beyond Sentiment we can classify and score language by:

  • Affect (level of emotion)
  • Assurance (level of confidence)
  • Technicality (level of subject-area specificity)
  • Partisanship (level of social organization embeddedness)
  • Fiat-ness (level of opinion-leading effort, as Rusty has been doing here)
  • … and thousands of other vectors we can conceive of

Looking exclusively or even primarily at Sentiment as your vector of meaning in a narrative map is almost always a recipe for confusion. For example, below is a short document marked up with ‘sentiment’ in green and red, but also with “Growth” vocabulary and “Value” vocabulary highlighted in fuschia ellipses.

Question: Is the Barclays report below ‘positive’?

Answer: No, if you like Value-oriented constructs like EPS and lower Operating Expenses.

Answer: Yes, if you like Growth-oriented constructs like Sales, Gross Margins and Non-GAAP revenue.

Taken alone, Sentiment tells you almost nothing. Combined with other vectors of narrative meaning, Sentiment can be one (of many) useful dimensions of narrative analysis.

So, please don’t be (or let your friends be) that person who thinks Japanese food is sushi – there’s a whole world of cuisine and language analysis out there to discover!

Learning #3: Graphs are our friend

To do the analysis noted above of ‘Sentiment’ or ‘Growthiness’ for a large corpus of documents, an auto-clustering graph can be very helpful to help prune outliers, boost the score of ‘canonical’ documents etc., but it is not necessary.

For other type of analysis graphs are super, super useful.

With a graph we can group (cluster) and measure the similarity of documents. For example, here is a graph that Rusty recently generated on the Inflation Narrative using Quid where he has clustered documents based on their linguistic similarity in order to see the key themes:

This is very, very helpful in order to be able to understand the Narrative.

Learning #4: The art of the graph

Once you really get into NLP clustering graphs you realize that there is a real art – a very human art – to conceiving of and extracting insight by observation from these graphs.

This is because (with flexible software) the permutations are, for all practical purposes, infinite. As a result, being hypothesis driven on the question you are asking, having domain expertise to craft a query, and then having a certain type of intuitive analytical ability to iterate the analysis creates a strong edge.

To try to give an example of this, we might have a question about how the future looks like for Facebook, and so we spin up a graph about this.

Starting with a graph clustered by topic we might then have a sense that (using some of the examples from Learning #2) it would be insightful to score and then observe the documents in the graph on the following dimensions:

  • Technicality
  • Affect
  • Confidence (see below for an old CIA mapping of language to probability)

Having done this, we can can then re-cluster and, for example, distinguish a ‘highly technical, low emotion, high confidence tightly clustered cluster’ from a ‘non-technical, high emotion, low confidence loosely clustered cluster’.

This is clearly valuable and very, very hard to do without a graph that is visualized.

Learning #5: Enter the Missionary

The worst sin of all is looking at Narrative without considering the Missionary.

The number of times someone has proudly told me “we analyze a gazillion tweets in real time with our super bad-ass data munger” … but in a way that does not distinguish whether there is, and who is/are, the Missionary(s) on a specific topic, and at a specific time …

Ben has written so extensively on this I will not re-hash here, but if you don’t know who the Missionaries are – on the specific topic, at a specific time – then it is very unlikely your Narrative analysis will be very fruitful (except in racking up AWS fees for munging massive, fruitless data sets).

Learning #6: Be the Centaur

We used to talk about Centaur chess – a combined human and machine intelligence as the most formidable chess player possible – until DeepMind ruined it for everyone by constantly winning with a machine intelligence alone.

But Narrative analysis is still way, way, way more complex than chess and so a Centaur approach is the right one for Narrative. But in a specific way.

Clearly, computers are useful for computing graphs based on language similarity.

To be clear we don’t absolutely need a machine to calculate a linguistic similarity graph: it is conceivable to take a stack of 500 documents (say, analyst reports), mark up each document with a highlighter by its unique n-grams, copy the n-grams onto a post-it note per document, score each n-gram on each post it note by a global tf-idf score, and then use our judgement to group post-it notes together by high score post-it notes.

But, man, would that be tedious, and probably not very accurate.

So, computers are useful.

But so are humans. Especially ones with domain expertise, quantitative abilities and non-linear creative minds (especially, non-linear creative minds).

Bottom line is that in all but the simplest systems, we are currently at the stage where the machine is best at generating a base map of current Narrative reality, but a human is better at then asking insightful questions of the map and / or predicting how the map will evolve and / or its interaction with ‘other bodies’ (see below), which then leads to actionable insight.

Learning #7: Narrative analysis in markets is a ‘three-body problem’, but with each body having a continuously changing, long-term unpredictable mass

Generally, simplistically, for the purposes of developing a system, we can think of Narrative in equity price movements as one body in a real life ’three body problem’:

  1. Fundamental information
  2. Technical information
  3. Narrative information

If we are going to get serious about building a system that performs in the real world, we must understand that Narrative information does not replace the first two or simply reflect the first two, but is a separate body.

This is really, really, really important.

(note: I add the ‘continuously changing, long-term unpredictable mass’ clause, as it does not seem conceivable to me that this ‘full system’ can be brute force ‘solved forwards’ like the classical physics example Ben described in his note The Three-Body Problem, whereas it does seem conceivable that the Narrative alone can be a Three Body Problem as Ben describes – more on this and quantum computing at the end of this note)

Now, please take a minute to accept or reject the notion of Narrative as one body in a ‘three-body – but with each body having a continuously changing, long-term unpredictable mass – problem’, as I feel fully resolved on this point and so am going to carry on this section with the ‘3rd separate body’ taken as fact (while considering that you should please feel free to add more bodies or sub-dive the first two to conform to your mental model of markets and make it an n-body problem, that’s not the important point, the important point is that Narrative is its own body in a three or greater body problem ) …

… so, accepting as fact Narrative as a third body and that the first two bodies are super well understood conceptually and many smart people are hyper-optimizing and hyper-incorporating the available information, it logically leads us to certain practical approaches, such as looking for periods when the other Fundamental and Technical informational inputs are ‘weak form’ and the Narrative information can become dominant and strong form.

If your interest in this stuff is making money then this is the really key point.

It’s not that the other forces will ever be zero, or that you can really predict how long they will stay weak (per the ‘continuously changing, long-term unpredictable mass’ clause), but when the Narrative is strong it will set direction (or cause volatility), and even if other forces such as new fundamental information re-emerge in an unexpected way, the Narrative will help create an asymmetric elasticity bound around price movement driven by the new information.

Inversely, as Ben pointed out, if you are betting on Narrative and the Narrative is very weak form (i.e., no one is looking) you won’t get paid.

Learning #8: Need more resolution!!!

Does the Narrative Machine work? – Yes, it works.

I’ve seen it so many times in so many different contexts with systems I would consider to be really quite naive delivering really surprisingly strong results. Oftentimes to the extent that I’m suspicious of the results and spend days and days digging into them. But invariably the results are the results, we can know why as it’s not a black box, and by working hard the systems improve.

So, if it works and is relatively un-picked over, then why isn’t everyone doing it?

I think there are three main reasons:

1. Mental model

Ad agency folks, political campaign folks, Department of Defense folks and most creatives seem to get it very well, so it is not a general mental model problem, but I have bumped into a distinct mental model problem around Narrative for hedge fund / markets folks:

Candidly, the mental model problem is so strong I have pretty much avoided discussing this ‘computational approach to Narrative’ stuff with hedge fund folks for the past year or so as it almost always goes the same:

  • The fundamental-type people tell me about their ‘process’ and require the Narrative to fit into their existing process as a subservient input into their mental model (including crypto traders which is … well … sobering) as, for example, a way of improving the timing or sizing of what they are already doing.
  • Meanwhile, the ‘quantitative’ folks just seem bemused that I’m so excited about graphs and say ‘we already do NLP and have a proprietary model’. Considering the amount of time and money it takes to get this to work as an even half-decent system (at least in the way that I’m describing it), this seems improbable given the resourcing and backgrounds of technical people at most funds I’ve met, at least as of 12 – 18 months ago.
  • The ‘quantamental’ folks are truly the worst, as they are obsessed with reducing Narrative to a ‘factor’ in a regression alongside thousands of other structured data sets.

To be clear, my experience is that all categories mentioned above are full of very smart people, but somehow the mental model I believe is true about the Narrative just doesn’t seem to fit with the mental model of folks who build systems for trading / investing in markets for a living. Whereas folks who market shampoo and yoga pants for a living seem to really quickly and intuitively get it. Odd, perhaps, until you consider that FMCG ad agency people, DoD intelligence analysts, and political campaigners all truly live the Narrative and are relatively unencumbered by the Physics Doctorate and MBA predictive analytics orthodoxy.

Anyway, as I started with, it was actually this point as noted by Ben in We’re Doing It Wrong that prompted me to write this note.

2. Quite a bit of time and money investment

As noted above, building a scalable, reasonably accurate, extensible system that will allow you conceive of fairly arbitrary Narrative questions, quickly spin them up, and have the system learn from them is, in my view, at least a ‘15 strong engineers for 12 – 18 months’ problem to get to V1.0. So ~$3M – $5M cost to get to a basic system that can then be built upon, and then at least a $5M run rate cost (ideally more) to keep extending, with really interesting results taking a couple of years to get to. So, a reasonable sized dollar and time commitment for a new approach.

I think this is why you really don’t see many shops building their own systems and rather see people using sell-side text analytics tools that are relatively thin applications (i.e. they are ‘hard coded’ to extract certain features from certain types of reports which are then output as a ‘buy/sell’-type signal) or 3rd party point solution providers (like DataMinr, Bottlenose etc.) which are focused on single point solutions sold to many clients (e.g., Dataminr for getting early warnings of event by processing Tweets) but are not true Narrative solutions as we are talking about it here.

3. Still low resolution

The first two reasons given here are primarily bias barriers, but there is a 3rd barrier – a technical barrier – which is very real: Resolution.

Per above, for about $5M a year you can build and run a decent, fit-for-purpose, reasonably flexible system for this type of analysis.

However, make no mistake about it, the resolution with which you can see the Narrative through this system is relatively low, and your ability to take a constant stream of images (graphs) and in particular to compare images (graphs) is very, very limited.

My sense is that we are today at the animation equivalent of ~6 dpi, ~1 frame a minute. It’s blurry, you can kind of follow the story, but you gotta interpret (guess) quite a bit and it is pretty painful after a while.

Unfortunately, it is just a basic physical fact that comparing large, complex graphs using classical computing is very, very compute intensive (at least with currently available algorithms). So this puts a limit on the computational approach and is why human interpretation is still critical in anything other than quite simple systems.

To be clear, these low-res Narrative observation and calculation systems work really surprisingly well and you can make money from them, they are just nowhere near what they can, should and will be.

So, how will we get to the full Narrative Machine?

To my mind this ability to compare complex objects at low cost and at high frequency will be the ‘killer app’ of quantum computing and will make this stuff really work.

I remember speaking on the same bill as quantum computing companies D-Wave and 1Qbit two or three years back (an age ago in quantum computing land!) and even then this hit me as absolutely true. This 2016 paper by 1Qbit sets out the case well.

We are not there yet with QC, but my bet is that we will get there within the coming years and that we will then truly, finally be able to achieve the Narrative Machine.

To calibrate what an acceleration of compute power looks like, I leave you with this image of the evolution of Lara Croft rendering resolution as a function of GPU processor improvement.

My point here is a simple one: once a technology starts on a path of increasing resolution, it ALWAYS follows a Moore’s Law-esque trajectory of improvement, with uses and implementations at higher levels of resolution that were never even considered in early days. It took 17 years for Lara Croft to become a digital character indistinguishable from imagery of a human actor. It won’t take nearly that long for a similar resolution intensity of Narrative-space.

Is it early days with the Narrative Machine?

Yes. But not as early as you might think.

And the future will be here faster than you suspect.


In the Flow – Real World vs. Market World


Real World vs. Market World

The price action of a holiday week at the end of the year is usually a humdrum affair. Volumes are muted, portfolios are getting locked in, no one is looking to take on risk … but not this year. The last week of 2018 was a rollercoaster. A gruesome Monday, a wonderful Wednesday, and a defibrillating Thursday and Friday. Does it mean anything?

It means that our narratives are in flux. It means that, from an Information Theory perspective, there are limited informational barriers that constrain the market equilibrium from moving sharply up or down. When narratives are in flux, market price levels are like a marble on a glass table top. It takes very little strength in a new signal to push the marble a long way in either direction.

It reminds me very much of market dynamics in the summer of 2013, when I first wrote about unstable market price levels from an Information Theory perspective. Then as now, we were getting big moves up and down in the market, often big intraday moves, without any obvious “news” to blame or credit for the action.

The narrative uncertainty in 2013 was all about the Fed’s Taper and the resulting market Taper Tantrum. What did it mean? Was this only a market phenomenon, or did it have some great significance for something happening in the real economy?

Today’s narrative uncertainty is similarly about Fed balance sheet policy, now actively rolling off their holdings and not just slowing the pace of new purchases as in 2013. What does it mean? Is the December sell-off only a market phenomenon, or is it related to some impending recession in the real economy? Or is it maybe even a harbinger or some financial system risk?

What struck me from a narrative formation perspective this past week was the absence of drumbeating about a real economy recession. Instead I saw the early signs of a market-positive narrative being formed: “our recent sell-off is a market problem, not a real economy problem”.

The fact is that the Chicago ISM number last week was VERY strong. The US economy sure doesn’t seem to be rolling over. More importantly, you saw a few reasonably powerful Missionaries (Mohamed El-Erian, for example) talking about this strength. My bet is that this narrative effort continues this week … that you’ll hear and read the usual suspects talking about how the real economy is in good shape, how consumer spending is strong, yada yada yada. All in very sharp contrast to the OMG-we’re-going-to-be-in-a-full-bore-recession-by-March articles you were reading three weeks ago when everyone was trying to push Jay Powell into playing Santa Claus.

So what to do if the narrative plays out this way?

I’m still not interested in getting bulled up on equities so long as real economy strength will be seen as encouragement for further Fed tightening, and so long as we remain engaged in a giant game of Chicken with China. I’m not saying to short the equity market. I’m saying to reduce gross exposure in the equity market, whatever that means to you. You don’t have to dance every dance.

Treasuries, on the other hand …

As you hear “this is a market problem, not a real economy problem”, you will see Treasuries sell off.

That’s the trade as this narrative develops.

It all goes back to the central actionable idea of Things Fall Apartmake your long-dated government bonds a tactical holding. Because unless we’re in the throes of a recessionary fearfest – and that’s exactly where we were going into the December Fed meeting and its immediate aftermath – long-dated government bonds are no longer able to play the same diversifying role in your portfolio that they have for the past 30 years. But the recessionary fearfest narrative is now declining, not growing. And that means that Treasuries will have a really hard time working.  

PDF Download: In the Flow Dec. 31, 2018


In the News | Week of 12.31.2018


In the News highlights key news stories from the prior quarter for companies announcing earnings over the next week, or for other major economic announcements. These stories are not the most read or the most important, but they are the most representative of the stories that mention these companies and events.

Lamb Weston

It’s a Fry Delivery Revolution

Lamb Weston’s earnings come in strong, but potato crop issue offer uncertain outlook

Lamb Weston Reports Growth, but Expects Challenges Next Year

Non-Farm Payrolls and Unemployment

The decline of the suburban American paid-on-call fire service

Jobless Rate Rises As Job-Seekers Pool Grows In Pennsylvania

United States : Tennessee Unemployment Drops Ahead of Holiday Season

Philly Fed: State Coincident Indexes increased in 43 states in November

Idaho’s Unemployment Rate Remains Below 3%

7 Reasons to Be a Bullish Market Contrarian


Rock, Paper, Scissors


Three weeks ago, I wrote a note titled “We’re Doing It Wrong“, which took to task the embedded anthropomorphic fallacies in how we use the massive computer processing capabilities now at our fingertips, what’s usually called artificial intelligence (AI) but is better named Big Compute. I won’t repeat all of that note, but here’s the skinny:

We think of markets as a clockwork machine, as an intricate collection of gears upon gears. We believe that if only we examine the clockwork closely enough, we can identify some hidden gear or unbeknownst gear movement that will let us predict the clockwork’s movement and make a lot of money.

Our model of markets is The Machine, and every Machine has a deterministic set of algorithms that create and drive it. Every Machine has an Answer.

This model – the market as machine – is an anthropomorphism.

If you use computers in your investment research process – and I know you do – I will bet you umpteen zillion dollars that you have those computers looking at structured historical data in an effort to find some repeating pattern. I will bet you do this rigorously and intentionally if you’re a quant. I will bet that you do this all the same, but non-rigorously and haphazardly if you’re not a quant.

Whether you realize it or not, you are using the market-as-machine model. You are looking for the Answer. Go on, you can admit it. You’re among friends here. I’m like Big Lou in the insurance ads … I’m one of you. It is embedded in our minds and in our businesses. Mine, too.

But here’s the thing.

If you use AI as just another input to that market-as-machine investment research process, you will get puzzling “results” that don’t help you very much. It will be just like using a telescope to get better measurements of the retrograde motion of Mars as it orbits around the Earth in your Ptolemaic model.

You will be disappointed by AI.

This is a note about how not to be disappointed by AI.

This is a note about how we can do it right.

And it starts with the game of Rock, Paper, Scissors.

I think we’re all familiar with this classic two-person game of strategic interaction, where on the count of three each player makes a hand sign for either Rock (a fist), Paper (a flat hand), or Scissors (two pointed fingers). Rock beats Scissors, Paper beats Rock, and Scissors beats Paper. It’s a game with (obviously) an enormous amount of luck to it, but human behavioral foibles and our poor ability to be truly random with our choices allow skilled players to do better than luck alone would predict. And as you might expect in a world where we are prepared to wager and compete on anything, aficionados of Rock, Paper, Scissors make up a highly devoted global sub-culture, complete with professional tournaments and dedicated YouTube channels and the like. It is a particularly powerful social phenomenon in Japan, where the game is called janken, and as likely as not any sort of minor disagreement or preference dispute among friends will be settled by a quick countdown and throw of hands.

So you can imagine the popular interest when a robotics lab at the University of Tokyo recently publicized its development of a machine that has a 100% win rate against human opponents in janken.

Fine, go ahead and build a computer to beat the reigning world champion of Go. But a computer that wins every janken game it plays? This is madness!

Luckily for the professional Rock, Paper, Scissors tour, however, this janken machine won’t be dominating tournaments anytime soon. Why not? Because it cheats.

Ishikawa Sanoo Laboratory, University of Tokyo (http://www.k2.t.u-tokyo.ac.jp/fusion/Janken/index-e.html)

The janken robot takes ultra high-speed images of the human player’s wrist and hand as it descends on the count of three, normalizes these images to account for different hand shapes and movement speeds, computes the final hand shape determined by the muscle and finger movements of the descending hand, and shapes its robotic hand into the winning shape before the human hand finishes its descent. The entire process takes about 20 milliseconds, with one of the toughest problems being slowing down enough so that the computer hand is formed at the same time as the human hand.

Twenty one-thousandths of a second.

Why do I love the cheating janken robot so much?

First, because it embodies what AI looks like. AI is not a giant brain. AI is massive computing power trained to “see” a little piece of the world in a way that the human brain simply cannot.

Second, because it embodies the best quote of one of the best investors of the 20th century, Chancellor Palpatine George Soros.

I’m not predicting. I’m observing.

The janken robot is not a predictive machine, it is an observing machine. It is not evaluating the Rock, Paper, Scissors playing history of the human opponent in hopes of finding some predictive historical pattern of strategic choice. It is purely a forward looking machine with no need to analyze past plays of the game. There is no structured data to be processed here. There is no place to plug in a regression. It is all reaction, all the time.

The janken robot is not SOLVING for the future behavior of its opponent.

The janken robot is CALCULATING the future behavior of its opponent.

It’s a subtle distinction, but it’s a distinction that means everything for a revolutionary use of AI in understanding the future of complex social systems like markets and politics.

Doing it wrong means using our Big Compute superpowers to identify predictive algorithms by which we can solve for the market’s future.

Doing it right means using our Big Compute superpowers to visualize social behaviors by which we can calculate the market’s future.

Here’s a rough schematic for what a janken machine would look like in an investment context.

I first wrote this up in 2011, and I know that a lot of this design has been leapfrogged even in a schematic sense. It also pre-dates all of our work on the Narrative Machine, and I’ve intentionally left out some key parts in this schematic. Also, I know that a lot of this schematic will be gibberish for some readers no matter how much I explain it, and trivial for some readers no matter how little I explain it. So don’t @me. But here’s the basic gist.

Start at the top center where data flows into the machine. By messages I mean both structured data (prices, tickers, etc.) and unstructured data (publications, transcripts, news feeds, etc.). The replication server copies and shunts the message flow to three different AI areas: A (what hand shapes win this super-janken game?), B (what hand shapes will the other players make next?), and C (executive function to tell A and B how to work together).

Both AI A and AI B have a Complex Event Processing (CEP) module at their core, which is just a ten dollar phrase for an AI that’s been trained to evaluate the connections between disparate data from disparate sources in real-time or near real-time.

The Bot Queue in AI A is a set of static data models generated by AI C, the executive controller. Think of these as small probing trades that are constantly being made on different time frames to see what works. That is, we don’t have a constant rule set for what “wins” in markets like we do in Rock, Paper, Scissors. But we can observe what rules work NOW.

The Game Specification in AI B is a set of dynamic data models generated by AI C, primarily different iterations or flavors of the Common Knowledge Game. Think of this as the AI in the janken robot that recognizes different preliminary movements of the human wrist and hand and calculates where that hand will end up. Again, this can be designed to play out over whatever time scale you like, although the computing power required for an effective calculation gets larger and larger the farther out into the future you go.

Do we remember what we’ve done? Do we learn? Sure, and that’s where AI C, the executive controller, comes into play. But we’re remembering and learning with a totally different purpose than any other current AI implementation in financial services. This machine is relentlessly forward-looking and actor-oriented. We don’t care about the behaviors of the past. We don’t care about the statistical arbitrage of structured data and squeezing out a few dimes from ephemeral anomalies. We care about observing the unstructured conditions of the present and understanding the physics of what hand shapes will be made next. Because those micro behaviors of fear and greed can NEVER be arbed away.

Is this an expensive project? No, we’re not building a multi-billion dollar supercomputer to simulate nuclear weapon design. But yes, it’s a low to mid single-digit million dollar price tag to do this right.

We’d like to build this machine in 2019.

We’d like to find a strategic partner to build it with us.

If that’s a serious conversation you’d like to have, drop me an email at [email protected] or DM me @epsilontheory.

This isn’t a Star Wars thing. This is a now thing.


Lord Make Me Chaste … But Not Yet


Back in 2013, when I was just starting Epsilon Theory, I would go around and give talks to institutional and professional investors about the power of narrative, particularly the narrative of Central Bank Omnipotence. In plain terms, that’s the idea that markets were up, up, up in largest part because everyone believed that everyone believed that monetary policy determined market outcomes. It wasn’t that central bankers are all-wise and knew the answer to everything. It’s not a narrative of Central Bank Omniscience. No, it’s that central bankers are all-powerful, Omnipotent, and their actions (or inactions) make the market go up (or down). That’s the narrative.

The most common reaction to my talk?

You’re nuts.

Only five years ago, soooo many institutional and professional investors did not believe that a faith in the Fed (the Jupiter of the central banker pantheon) was the Why for a bull market. Particularly among value-oriented investors and old-school hedge fund managers like Lee Cooperman and Stan Druckenmiller, I heard a very different refrain – the US economy was now in “a self-sustaining recovery”, and that was Why stocks were up.

Sure, these Masters of the Universe followed the Fed closely and didn’t deny its importance in setting the broad parameters of real-world business cycles. Sure, they read every word that this Jon Hilsenrath fellow over at the Wall Street Journal would write about Fed intentions, and sure, they went to Big Bank X, Y and Z’s small group dinners with Fed governors A, B and C, and sure, they understood that Europe was safe now that Mario Draghi was adopting the Fed playbook, but being influenced by such as that was for lesser mortals. No, no … what I had to understand, dear boy, is that they watched certain infallible market and macroeconomic signals, coupled that with a bevy of analysts and PMs doing “deep fundamental dives” into individual stocks, and THAT was why they were Masters of the Universe.

Fast forward to 2018. The infallible market and macroeconomic signals have, in fact, failed. All those buy-side analysts and PMs finding “alpha” from their 30 worksheet-long FCF models and their oh-so sharp questions posed to management at 1×1 meetings and their oh-so observant site visits to this facility or that facility have, in fact, been fired. All of these Masters of the Universe like Lee Cooperman and Stan Druckenmiller have turned their hedge funds into “family offices”. Not because they WANTED to. Because they HAD to.

I’m not picking on Cooperman and Druckenmiller. Really I’m not. This is a story that’s been repeated a thousand times over the past five years, in both big and small ways. But both Cooperman and Druckenmiller, for whatever reasons, have kept themselves very much in the public eye and the public discourse since winding down their firms, trying to Master of the Universe-splain what happened.

Lee Cooperman and Stan Druckenmiller say it’s the machines’ fault, that “algorithms” have taken over the markets they loved so much and understood so well. It’s those darn machines. That’s why they can’t beat the market any more. That’s why their infallible signals failed and why they had to fire all those brilliant analysts.

That’s why, with the fervor that only a religious convert can possess, these old school Masters of the Universe now pray to their Fed gods, because only the Fed can control the machines. Only the Fed can say the right words and do the right things to make the machines behave.

How do these prayers manifest themselves?

In forms like every Missionary appearance on CNBC last week, much less Jim Cramer’s rantings and ravings, beseeching Jay Powell and the Fed to show mercy on our portfolios and stop the madness of … {checks notes} … 2.5% short-term interest rates.

In forms like last Sunday’s op-ed in the Wall Street Journal from Stan Druckenmiller and former central banker Kevin Warsh, saying that awkshuallly, 2011 was the time to exit QE, and an economy with >3% growth and an unemployment rate <4% is just too “fragile” to withstand the “double-barreled blitz” of higher interest rates and a rolling-off balance sheet.

Honestly, it’s embarrassing, especially for self-styled Fed critics like Druckenmiller, who has railed against QE and extraordinary monetary policy actions for YEARS.

It’s like St. Augustine, who prior to his conversion to Christianity in his early-30s, was quite the ladies man. As he recalls his prayers from those wayward days: Lord, make me chaste. But not yet.

The Druck equivalent: Lord, give me QT. But not yet.

I thought there was a good chance that Jay Powell would answer those prayers.

He didn’t.

All Powell needed to do was say the magic words: “we are watching carefully”. Watching US-China trade negotiations carefully. Watching the strength of the dollar and the weakness of commodities carefully. Watching EVERYTHING that Mr. Market is oh-so worried about carefully. Because if the Fed is “watching carefully”, then the Fed has got our back. And there’s nothing better for an investable rally than knowing that the Fed has got our back.

But Powell didn’t say this. He didn’t say anything close to this. In Fed-speak, he came pretty darn close to saying the opposite of this.

Powell told you on Wednesday that the Fed does not have your back.

Will the Fed be there for us in the End Times, when things get REALLY bad? Of course they will. That’s THE job of any central bank, to provide emergency liquidity to markets when the Four Horsemen of the Investment Apocalypse come galloping into town.

But will THIS Fed have your back in the way that everyone from Druck and Cramer and Trump and every other Wall Street rich guy has come to expect over the past 9 years?

Will THIS Fed define the health of the US economy by whether the stock market is up bigly or not?

Will THIS Fed do “whatever it takes” to outlaw the business cycle and prevent a recession from ever happening again?

Powell said no.

Good for him.

But if the Fed does not have our back, then we are well and truly stuck in a game of Chicken between Trump and Xi … hell, we’re stuck in a game of Chicken between Trump and the world.

You have no edge in this game. You don’t know the odds of this game. Not because you’re not smart enough and not because you’re not trying hard enough, but because the edge and odds in a game of Chicken are unknowable. And anyone who tells you otherwise is lying to you and/or lying to themselves.

The problem for markets today is not the Fed.

The problem for markets today is the guy in the White House.

Maybe his game of Chicken with the world ends up great for the US. Maybe it’s a necessary game to play. I don’t think it will and I don’t think it is, but no one asked me. I’m along for the ride, like it or not.


What I can do, though, is refuse to play along with the narrative charade. What I can do, though, is put my faith in my pack and my own actions. What I can do, though, is step back from the market casino while these games of Chicken play out.

You can, too.

Clear Eyes, Full Hearts, Can’t Lose.


In the Flow – Sometimes the Answer to a Prayer is “No”


Sometimes the Answer to a Prayer is “No”

Back in 2013, when I was just starting Epsilon Theory, I would go around and give talks to institutional and professional investors about the power of narrative, particularly the narrative of Central Bank Omnipotence. In plain terms, that’s the idea that markets were up, up, up in largest part because everyone believed that everyone believed that monetary policy determined market outcomes. It wasn’t that central bankers are all-wise and knew the answer to everything. It’s not a narrative of Central Bank Omniscience. No, it’s that central bankers are all-powerful, Omnipotent, and their actions (or inactions) make the market go up (or down). That’s the narrative.

The most common reaction to my talk?

You’re nuts.

Only five years ago, soooo many institutional and professional investors did not believe that a faith in the Fed (the Jupiter of the central banker pantheon) was the Why for a bull market. Particularly among value-oriented investors and old-school hedge fund managers like Lee Cooperman and Stan Druckenmiller, I heard a very different refrain – the US economy was now in “a self-sustaining recovery”, and that was Why stocks were up.

Sure, these Masters of the Universe followed the Fed closely and didn’t deny its importance in setting the broad parameters of real-world business cycles. Sure, they read every word that this Jon Hilsenrath fellow over at the Wall Street Journal would write about Fed intentions, and sure, they went to Big Bank X, Y and Z’s small group dinners with Fed governors A, B and C, and sure, they understood that Europe was safe now that Mario Draghi was adopting the Fed playbook, but being influenced by such as that was for lesser mortals. No, no … what I had to understand, dear boy, is that they watched certain infallible market and macroeconomic signals, coupled that with a bevy of analysts and PMs doing “deep fundamental dives” into individual stocks, and THAT was why they were Masters of the Universe.

Fast forward to 2018, five years later. The infallible market and macroeconomic signals have, in fact, failed. All those buy-side analysts and PMs finding “alpha” from their 30 worksheet-long FCF models and their oh-so sharp questions posed to management at 1×1 meetings and their oh-so observant site visits to this facility or that facility have, in fact, been fired. All of these Masters of the Universe like Lee Cooperman and Stan Druckenmiller have turned their hedge funds into “family offices”. Not because they WANTED to. Because they HAD to.

I’m not picking on Cooperman and Druckenmiller. Really I’m not. This is a story that’s been repeated a thousand times over the past five years, in both big and small ways. But both Cooperman and Druckenmiller, for whatever reasons, have kept themselves very much in the public eye and the public discourse since winding down their firms, trying to Master of the Universe-splain what happened.

Lee Cooperman and Stan Druckenmiller say it’s the machines’ fault, that “algorithms” have taken over the markets they loved so much and understood so well. It’s those darn machines. That’s why they can’t beat the market any more. That’s why their infallible signals failed and why they had to fire all those brilliant analysts. That’s why, with the fervor that only a religious convert can possess, these old school Masters of the Universe now pray to their Fed gods, because only the Fed can control the machines. Only the Fed can say the right words and do the right things to make the machines behave.

How do these prayers manifest themselves?

In forms like every Missionary appearance on CNBC last week, much less Jim Cramer’s rantings and ravings, beseeching Jay Powell and the Fed to show mercy on our portfolios and stop the madness of {checks notes} 2.5% short-term interest rates. In forms like last Sunday’s op-ed in the Wall Street Journal from Stan Druckenmiller and former central banker Kevin Warsh, saying that awkshuallly, 2011 was the time to exit QE, and an economy with >3% growth and an unemployment rate <4% is just too “fragile” to withstand the “double-barreled blitz” of higher interest rates and a rolling-off balance sheet.

Honestly, it’s embarrassing, especially for self-styled Fed critics like Druckenmiller, who has railed against QE and extraordinary monetary policy actions for YEARS. It’s like St. Augustine, who prior to his conversion to Christianity in his early-30s was quite the ladies man, recalling his pre-conversion prayers: Lord, make me chaste. But not yet.

The Druck equivalent: Lord, give me QT. But not yet.

I thought there was a good chance that Jay Powell would answer those prayers. He didn’t.

As I wrote last week, Powell had a clear path on Wednesday to supporting markets in a big way. He could hike by 25 bps as planned, and then all he needed to do was say the magic words: “we are watching carefully”. Watching US-China trade negotiations carefully. Watching the strength of the dollar and the weakness of commodities carefully. Watching EVERYTHING that Mr. Market is oh-so worried about carefully. Because if the Fed is “watching carefully”, then the Fed has got our back. And there’s nothing better for an investable rally than knowing that the Fed has got our back.

But Powell didn’t say this. He didn’t say anything close to this. In Fed-speak, he came pretty darn close to saying the opposite of this.

Powell told you on Wednesday that the Fed does not have your back.

What does that mean? It means that for the next two months, the market will zig and zag like crazy on every piece of US-China “news”. And the US-China narrative is getting worse, not better. Last week, the Justice Department unveiled criminal charges against two Chinese intelligence operatives for hacking, among others, the US Navy. Apparently there was some pact signed between the US and China back in 2015 where both sides pledged not to do this sort of thing, and apparently the Chinese reneged on that agreement.

My point is not that this isn’t a bad act by the Chinese. My point is that indicting Chinese spies living in China has exactly zero real-life consequences for the spies and exactly zero chance of prosecution. It is purely an exercise in narrative creation. It is purely a discretionary choice by the US government in timing and scope and publicity, all aimed at a domestic US audience. The Trump Administration is preparing you for a hard(er) line against China, because now it’s not an economic fight, it’s a national security fight. This is a clear emerging narrative in our analysis.

National security always trumps markets and economics. Always.

US-China relations got significantly more Cold War-ish this past week, which increases the downside pay-offs of any negotiation equilibrium that ultimately emerges here.

So what’s the action plan? We are back to our regularly scheduled entertainment of all Trump-Xi game of Chicken, all the time. That means you are faced with technical uncertainty in markets, not mere investment risk. That means that every rally is to be sold. That means that you take down your gross exposure.

This is a recessionary scare. This is a global trade scare. Financials won’t work here. Cyclicals won’t work here (yet). You might think that Tech would work here, because secular growth is at a premium in a recession, but the global supply chain issues for Tech are pretty daunting. If you’ve got to be long something, take a look at Consumer Staples (our latest In Focus note for ET Pro subscribers), and take a look at Healthcare (our next In Focus note).

PDF Download: In Summary Dec. 15 – Dec. 21, 2018


In the News | Week of 12.24.2018


In the News highlights key news stories from the prior quarter for companies announcing earnings over the next week, or for other major economic announcements. These stories are not the most read or the most important, but they are the most representative of the stories that mention these companies and events.

It is Christmas week – and the last full week of the year – which means we have very little in the way of upcoming events in financial markets. Despite our expressed antipathy for financial market prediction pieces, year-in-review pieces covering other topics are far more fascinating. Gell-Mann Amnesia at work? Either way, below is a finance-free and Trump-free (mostly) list of what people have been writing about 2018. As always, these are the most representative, not the most read, highest quality, etc:

The Facebook Dilemma: A Year in Review

INTERVIEW: Todd Mozer, CEO, Sensory – On Biometrics, AI and Connected Cars

The Best TV Performances of 2018

2018 year in review: 50 stories from 50 states that moved us

Google’s year in review: All of the highlights and lowlights from 2018

Year in Review 2018 and Year to Come 2019 – United Kingdom Law

TradeWeb Year in Review

Catholic News: A Year in Review Around the World

MEI 2018 In Review


The Prediction Polka

Source; South Park Studios

It starts in late November with the early birds. But this week – the week before Christmas – is when it reaches a fever pitch. What am I talking about?

The Great Holiday Snack Feast, of course.

The break room of every pension fund, family office, endowment and RIA that hasn’t gone fully passive is a buyer’s market for junk food. It used to be mostly popcorn tins – you know, the ones with the festive holiday scene on the side, and two or three different flavors available on the inside. Life pro-trip: always go with the white cheddar. Your New York-area funds are always good for a selection of toffees from Bridgewater Chocolate. No relation to the macro fund, apparently. Managers in non-traditional financial centers usually come up with more unique offerings. These range from the mundane (e.g. sausage and cheese from the Midwest) to the extraordinary (e.g. Goode Company Pecan Pies from the Gulf Coast) to the surprisingly good (e.g. some weird thing called a Yule Log from your friends across the pond) to the risky (e.g. a bottle of whiskey from the odd Kentucky or Tennessee shop) to the ill-advised (e.g. a literal smoked salmon I saw sent from Minnesota – really, people?).

But no Christmas gift for the asset owner or his advisers is as predictable as, well, predictions.

And yes, as I wrote earlier this week, the season of predictions is also the season of snark. But just like that In Brief, this one won’t be criticizing the accuracy of past predictions or making fun of some of the new predictions being made for 2019. As you start to read these pieces, however, I want you to bear something in mind: nobody uses them.


Those recession probabilities from an economist at a sell-side shop or standalone research house – something one of Ben’s and my new favorite bloggers brought up today – is anyone dropping those assumptions into asset allocation models? The predictions on year-end S&P 500 and 10-year levels? Odds on this outcome or that from the China trade war negotiations? Who is making adjustments to model portfolios or strategic asset allocation plans for new clients going into 2019 based on all these brilliant research pieces?


OK, sure, maybe there is a financial adviser or two out there who really is adjusting his positions because this research house or that thinks that this is where levels are going to be at year end. But that’s not what these are for. That’s not what these are really about. At every level, the Prediction Polka is a sales tool and nothing else.

The best way to understand this very odd thing that we do is (as so many things are) through the immortal genius of Trey Parker and Matt Stone. In an episode called Cash for Gold, the South Park boys walk viewers through a fanciful version of the low-end gold jewelry purchase-gift-and-exchange-for-cash cycle. It is a process, much like the market prediction racket, in which no one actually wants the product, but in which everyone needs to sell the product. The video, which is obviously offensive in three or four different ways – it’s South Park, y’all – is must watch, even if it does require you to install Flash like some kind of 20th Century barbarian.

The basic idea in our industry is similar, and it goes something like this:

Now, we’re not saying that all research is useless, and even if you are skeptical of its utility, every watcher of narratives should be mindful of how the sell side and buy side alike enjoy their roles as missionaries to sell their clients on the Next Big Rotation Trade. Our money is on Health Care being the name pulled from a hat in 2019, but it’s always a bit of an adventure. But in this case, if we are talking about predictions on the S&P, the 10-year, and probabilities of this major economic event or that, better to see them for what they are: sales tools all the way down. They’re usually not missionary vehicles. They’re not really vessels for narrative. They’re the thing-that-everyone-must-do-to-look-informed-to-clients.

So whereas we’d usually tell you that you don’t get a pass from understanding what this kind of published content says about what the crowd knows that the crowd knows, in this case, here’s your pass. Spend some time with the kids or that bottle of holiday whisky. If you’re in the market for a nice value on peated whisky, I’m a fan of the Longrow Peated. If you favor a dram that’s heavier on the sherry, I think that the Aberlour A’bunadh – despite being a part of the whole no-age-statement crazy – may be the best value in Scotch today.



Twilight of the (Consumer) Goods?


As detailed in the November monitor updates and In Focus notes from Ben, we have observed some evidence of what we believe is a more significant transition from inflation as the dominant narrative influencing financial markets to narratives of (1) recessionary fears and (2) tariffs and trade, especially vis-à-vis China. As the latter, in particular, emerges into common knowledge, we expect that 2019 “outlook” commentary and sell-side chatter will reinforce predictions and advice that are mostly about avoiding exposure to those risks.

We have been clear about our game theoretic view of predicting the outcome of the China trade war: We think it is a chicken fight which provides practically no ability to assign odds. Still, regardless of any fundamental impact from the outcome, the narrative shift remains meaningful to asset prices. In our view, rather than attempting to predict those outcomes, investors may benefit from considering companies, sectors and assets which would benefit from reduced attention to inflation and growth narratives. The most obvious candidates in our minds are brand-oriented consumer stocks, and staples in particular. These are companies for which rising costs have been an increasing, nearly universal concern. They are also stocks for which narratives about the Death of Brands have been gnawing at growth-hungry investors for some time. They are also stocks which, despite an increasingly difficult environment for stocks in 2018, have not delivered on their historical defensive traits.

To explore this further, we constructed a universe of nine large cap brand-oriented, primarily staples companies with consistently robust media and research coverage over the last four years. General Mills, Altria, Kimberly-Clark, Kraft Heinz, Clorox, Procter & Gamble, PepsiCo, Coca-Cola and Colgate-Palmolive comprise this universe. Indexing to 2015 coverage as a baseline, we measured the proportion of stories and research about each of the nine companies which focused on inflation or rising costs on the one hand, and which focused on slowing growth on the other.

Source: Quid and Epsilon Theory

In the aggregate for this group, discussion of inflation fears and slowing growth across all forms of published content has increased in each of the last four calendar years. The frequency of rising cost discussions has been 45% higher in 2018 than in 2015, while discussions of slowing growth have been 37% more common. Each phenomenon has varied in its impact on different companies within the universe. Costs-related commentary, for example, has risen dramatically for General Mills and Clorox, but has remained moderate for Procter & Gamble and Colgate-Palmolive.

Source: Quid, Epsilon Theory

Slowing growth language has increased even more broadly across the group.

Source: Quid, Epsilon Theory

Company-Specific Cases – General Mills

The clearest example of how this narrative has manifested is General Mills. The exhibit below covers the full period of 2018 (through December 19th). In general, this is a stock for which the most central narrative clusters are typically stability, quality, value or dividends. From time to time, of course, there may be news about a brand, a channel, a marketing initiative or some other material detail, but these rarely play a central role in the narrative for the company.

In 2018, this has changed.

Source: Quid, Epsilon Theory

This year, the strongest, highest attention, most central topics for General Mills were consistently related to input costs, food costs and freight costs (see the top two boxes in the narrative map below). The distinguishing trait of the other most central topics was an emphasis on either struggling brands (in this case, Yoplait) or actions taken to promote inorganic growth. While the usual suspects of value, quality, defensiveness and dividends have still been there in the background, the research, media and content investors are consuming when they review GIS are all about rising costs and slowing growth. Even published sell-side research, which usually pays a bit more mind to the traditional rationale for holding the stock, is clustered and positioned in the dead center of these topics.

Company-Specific Cases – Altria

The 2018 narrative of Altria, on the other hand, serves as an example of staples companies that have been less impacted by the influence of rising costs, and more by slowing growth across products. The significant drop in Altria share price has meant that “value” language commentary has remained alive and well for the company as one of the highest attention and interconnected nodes. But this emphasis is belied by the overwhelming topical emphasis elsewhere in the narrative structure, which is almost universally dedicated to competitive pressures and other matters limiting the attractiveness of Altria’s attempts to replace lost growth. The most central and most interconnected clusters of the Altria narrative are discussions of e-cigs and a resurgence in regulatory pressure that began in 2017.

Taken in context of a surprisingly intense focus in media – and even on the sell-side – on cannabis-related topics, we would characterize the 2018 Altria narrative as: “this company can’t grow, it’s getting competed and regulated out of its core businesses, and has in cannabis an emerging substitute where it is behind the curve.” If there is doubt that this has weighed on the company (and its management), you need only read the discussions within the recent reports of Altria’s significant acquisition of the equity in Juul Labs. Alternatively, you might consider the decision of equally beleaguered brewing companies to publicize potential cannabis-related collaborations.

Source: Quid, Epsilon Theory

As frequent readers will know, we are not in the business of publishing fundamental views. We do not know whether companies like Altria or General Mills will be successful in proving these narratives right or wrong, and readers should incorporate their own judgments on those points. We do, however, think that there are better-than-even odds that the shifting attention of market participants from growth and inflation narratives will relax the pressure on many brand-oriented stocks with cost, brand and growth issues.

For those conducting fundamental, trend- and sentiment-based analysis of these companies and the staples sector more broadly, we advise awareness of these changing narratives.

PDF Download: Twilight of the (Consumer) Goods?


The Road to Tannu Tuva, Pt. 1

Source: Atlas Obscura’s lovely atlas to this location,

There is a large, flat rock set into the gentle rise of a hill in a strange land called Tannu Tuva. Someone has carved a symbol into its face like some kind of ancient rune. But it is not an ancient rune. It was drawn there just this year by a man called Ralph Leighton. Likewise, Tannu Tuva is not some place out of fantasy. It is a Russian state that today is called the Tuvan Republic.

Those are the facts, anyway, but none of them is true. Not really.

In every way that matters, the stylized engraving of fermion scattering you see above, a famous illustration from the field of quantum electrodynamics called a Feynman Diagram, is an ancient rune. Even carved just months ago, it is at once both a mechanism for lucid communication and an esoteric symbol of one of the most mystical features of our natural world. Tannu Tuva, too, was and is a very real place. Yet it is also very much a place out of a fantasy, a conscious representation by a small group of scientists to the power of leaving the door to the unknown ajar, to the adventure of discovery, and to the commitment to overcoming barriers thought to be impossible.

You’ve heard of the man who first drew that diagram. You may have read much of his story. His name was Richard Feynman, and he is a genius. Or at least, he was until his death, 30 years ago last February.

Mathematician Mark Kac was also a genius. A Polish mathematician, Kac worked with Feynman at Cornell in the 1940s after Feynman left the Manhattan Project. Kac had arrived in New York after finishing his studies in Lwów in 1937. It was a near thing. His parents and his brother were murdered by Nazis in Krzemieniec in 1942. Each of Kac and Feynman pursued his own genius, Richard in theoretical physics and Mark in mathematics. But their mutual respect led to a significant joint achievement: The Feynman-Kac Formula.

The Feynman-Kac Formula may not be familiar to you. Among other things, it allows for some stochastic problems to be solved using a deterministic framework. In other words, it allows us to use formulas to solve a certain class of problems that would otherwise require us to simulate the system to reach a result. While it can’t be used, lamentably, to solve a Three-Body Problem, it is the kind of mathematical approach that permits us to solve some similar problems without resorting to number crunching on iterative calculations and simulations.

If you work in finance, have studied for the CFA or went to business school, you have probably unwittingly used the FeynmanKac Formula in what investors consider to be an important partial differential equation: pricing a stock option. Yes, by far the most famous application of the joint work of this mathematics wizard and physics genius is the Black-Scholes formula. Something tells me that being used primarily as a memorization test by second-year banking analysts to intimidate college seniors in Goldman Sachs interviews was not the fate Richard and Mark might have had in mind for their achievement. Their collaboration is still intriguing, not least because it gave us Kac’s description of what it was that made Richard Feynman so unusual.

In science, as well as in other fields of human endeavor, there are two kinds of geniuses: the “ordinary” and the “magicians.” An ordinary genius is a fellow that you and I would be just as good as, if we were only many times better. There is no mystery as to how his mind works. Once we understand what he has done, we feel certain that we, too, could have done it. It is different with the magicians. They are, to use mathematical jargon, in the orthogonal complement of where we are and the working of their minds is for all intents and purposes incomprehensible. Even after we understand what they have done, the process by which they have done it is completely dark. They seldom, if ever, have students because they cannot be emulated and it must be terribly frustrating for a brilliant young mind to cope with the mysterious ways in which the magician’s mind works. Richard Feynman is a magician of the highest caliber.

Enigmas of Chance: An Autobiography, by Mark Kac (1985)

Kac’s characterization of Feynman as the magician kind of genius is consistent with the observations made about Feynman by many others. Oppenheimer commented specifically on his unparalleled and unique relationship with both the theoretical and experimental physicists on the Manhattan Project, for example.  Al Seckel’s stories about Feynman famously included references to his interactions with Stephen Hawking. In one such story, Feynman dismissed Hawking’s ability to perform path integration in his head, since it was “much more interesting to come up with the technique like I did.” Creativity, not technical mechanics, was the secret to Feynman’s genius. His genius was a difference in kind, not in magnitude.

Another Feynman peer and contemporary during his years at Caltech was a physicist (and genius) named Murray Gell-Mann. He is most famous for his Nobel Prize-winning role in the development of our understanding of elementary particles. He is also famous, especially in our little publication, for his role in describing Gell-Mann Amnesia. Popularized by Michael Crichton, of all people, Gell-Mann Amnesia describes our ability to read with disbelief the poor quality of conclusions in a newspaper or magazine story about a topic we know very well, after which we turn the page to take in reports on other fields of expertise and nod along happily.

Gell-Mann had a productive but challenging relationship with Feynman. As a charming read published by the Atlantic in 2000 put it: “Dick and Murray, as everyone soon called them, became inseparable. Strolling Caltech’s immaculately landscaped campus or dueling at the chalkboard over some calculations, the two scientists discussed physics for hours.”  But the two were often at odds in matters of style and personality. They also approached the conventions of science – when you write up your findings, how early in your process you publicize your theories, how you publish – so differently that their partnership may have fallen short of what it could have been. At the least, it fell short of what many hoped. Gell-Mann, at once both confounded by Feynman’s form of genius and irritated by what he perceived as affectations of false-humility, playfully described Richard’s approach to science:

You write down the problem.

You think very hard.

Then you write down the answer.

It was a joke, and best told, as it was originally to Sidney Coleman and then to James Gleick in Genius: The Life and Science of Richard Feynman, with closed eyes and knuckles pressed to forehead, pretending to think very hard. 

And yet.

There is a simple truth in this logical process that is faithful to, if obviously a comically oversimplified version of, what science means. It also happens to be the process whereby human ingenuity is transformed into tangible output in almost every other technical and social field. The technical prowess of ordinary geniuses in Kac’s terminology, and the act of thinking very hard in Gell-Mann’s, are important to any science. They are necessary conditions for science to have really found out anything. Necessary but insufficient. The transmission mechanism which begins with our world and the people in it, and ends with some tangible or intellectual product that influences that world, requires two more things:

Someone to write down the problem.

Someone to write down the answer.

These are roles that exist not only in physics, medicine, mathematics and other natural sciences or practical derivations thereof, but in any field which relies upon the discovery of truths about the world, predictions about the implications of those truths, and the evaluation of the credibility of those predictions. By right, that ought to (and does) include the social sciences – political science, economics, sociology, anthropology, language and history. It also includes the critical field of journalism, and yes, the business of investing and capital management.

When Kac spoke of Feynman as a magician, he was speaking in large part of his creative capacity to visualize problems in a different way.  Much of Feynman’s reputation was built on his Nobel Prize-winning work with Tomonaga and Schwinger on quantum electrodynamics, but even more was built on Feynman’s illustrations that framed the analysis of those dynamics. He had a picture in his mind of what was happening in nature, and he constructed a language which not only helped to communicate those details, but which itself served to prod the discovery of the mathematics to describe it. A lifelong scientist whose conscious dabbling in visual art and music took place too late to amount to much beyond personal pleasure, Feynman’s greatest work was nonetheless a heavily symbolic creation of both art and language.

Yes. Feynman was a magician of the highest caliber.

The magician’s role – to be able to conceive the right questions to ask and right ways to ask them – is critical to every science, to investing and to journalism. It is also innately subjective. The reasons should be intuitive. We research what we think is important, what we think is interesting, and perhaps most importantly, what we think will pay the bills. The result is perilous for even the most well-meaning researcher. From the very beginning of each process of writing down our questions, our work is saddled with bias. The questions we ask, the stories we assign, the avenues of research we pursue and the investments we elect to evaluate are all heavily influenced by both external directives and our own judgments based on internal priors.

At each other stage of the research process – from analysis to interpretation – temptation is everpresent. We will naturally be more inclined to test theories which, if proven, fit with our other theories. We will naturally be more inclined to believe results which support a theory we favor. We will more easily see disqualifying flaws in data which do not support our theories, beliefs or priors. We will be tempted to inject non-falsifiable judgments and opinions we feel confident are self-evident into the gap between our findings and their implications.

We can never wholly avoid the injection of bias into our research. We can only adopt processes which seek to ruthlessly rip it out by root and stem.

When we, our colleagues and our peers are hell-bent on the falsification of every idea spawned by the questions we pose, and on expunging underdetermined explanations for some fact or theory about the world, we slowly erode the influence of that bias. What remains from that process is science. The true scientist, whether he be journalist, investor, physicist, mathematician or sociologist, does not stop research and challenge when he calculates a feature of some thing that is adequately explanatory. He stops when he and all the resources around him that can be marshaled can find no other way to destroy his idea.   

When the scientist, journalist or investor does not aggressively seek out the falsification of his ideas and the questions which led to them, a strange thing happens: The magician becomes a missionary for his priors. What remains from his process is not science. What remains are facts tenuously grafted onto a premise he rather fancied. What remains is scientism – the meme of science!

It is for this reason, I think, that Feynman’s most unique trait may be his commitment to the absolute scientific necessity of doubt. No simple Pollyannaish visionary charmed by the magic of discovery, Feynman was also a missionary of another variety. In his case, however, his mission was to promote the vitality of doubt. Richard was talking about scientism and the meme of science! before almost anyone. He called it “cargo cult science.”

There is one feature I notice that is generally missing in cargo cult science. … It’s a kind of scientific integrity, a principle of scientific thought that corresponds to a kind of utter honesty — a kind of leaning over backwards. For example, if you’re doing an experiment, you should report everything that you think might make it invalid — not only what you think is right about it; other causes that could possibly explain your results; and things you thought of that you’ve eliminated by some other experiment, and how they worked — to make sure the other fellow can tell they have been eliminated.

Details that could throw doubt on your interpretation must be given, if you know them. You must do the best you can — if you know anything at all wrong, or possibly wrong — to explain it. If you make a theory, for example, and advertise it, or put it out, then you must also put down all the facts that disagree with it, as well as those that agree with it. There is also a more subtle problem. When you have put a lot of ideas together to make an elaborate theory, you want to make sure, when explaining what it fits, that those things it fits are not just the things that gave you the idea for the theory; but that the finished theory makes something else come out right, in addition. In summary, the idea is to try to give all of the information to help others to judge the value of your contribution; not just the information that leads to judgement in one particular direction or another.

Surely You’re Joking, Mr. Feynman (1985)

In a world awash with narrative, it is precious and rare to know that a thing is what we say it is and not a glorified reflection of our priors and predispositions. It is even more rare, however, to come upon a process for taking in information as an investor or citizen that is capable of achieving that standard. Most investment processes – even many systematic strategies – are utterly incapable of achieving it. Most journalistic standards fall well short, too. Even many of our sciences, you will be unsurprised to discover, have empowered a great many shoddy analyses to exist through a combination of indifference and a lack of scientific integrity.  

Over the next three editions of this Notes from the Road series, I will draw a map of how I think we can sharpen our awareness of the science! meme at work. I will explore our engagement as investors, consumers of news and evaluators of findings from the natural and social sciences. I will also draw a map of how we can do better as primary actors in our own research processes. Because I think that that Richard would find some delight in a dead serious treatment of Murray Gell-Mann’s tongue-in-cheek description of the Feynman Method, that will be our model. Three notes to follow this one, each discussing the ways in which the science! meme creeps into each phase of an otherwise legitimate fact-finding undertaking – in the questions we ask, the analysis we undertake, and the stories we tell about it.

The next note, Part 2, will examine how what questions we ask and how we ask them influence the quality and direction of underexamined research in multiple fields. This will be the start on our Road to Tannu Tuva. But it is important that you know it isn’t just a road of rigor.

“Okay, then what ever happened to Tannu Tuva?”

“Tannu what?” I said. “I never heard of it.”

“When I was a kid,” Richard continued, “I used to collect stamps. There were some wonderful triangular and diamond-shaped stamps that came from a place called ‘Tannu Tuva.’ ”

… I straightened up in my chair a bit and said, “Sir, there is no such country.’

“Sure there is,” said Richard. “In the 1930s it was a purple splotch on the map near Outer Mongolia, and I’ve never heard anything about it ever since.”

Had I stopped and thought a moment, I would have realized that Richard’s favorite trick was to say something unbelievable that turns out to be true. Instead, I tightened the noose that had just been placed around my neck: “The only countries near Outer Mongolia are China and the Soviet Union, I said, boldly. “I can show you on the map.”

We opened…a map of Asia.

“See?” I said. “There’s nothing here but the USSR, Mongolia, and China.  This ‘Tannu Tuva’ must have been somewhere else.”

“Oh, look!” said Carl. “Tuvinskaya ASSR. It’s bordered on the south by the Tannu-Ola Mountains.”

Sure enough, occupying a notch northwest of Mongolia was a territory that could well once have had the name Tannu Tuva. I thought, I’ve been had by a stamp collector again!

“Look at this,” remarked Richard. “The capital is spelled K-Y-Z-Y-L.”

“That’s crazy,” I said. “There’s not a legitimate vowel anywhere!”

“We must go there”, said Gweneth

“Yeah!” exclaimed Richard. “A place that’s spelled K-Y-Z-Y-L has just got to be interesting!”

Tuva or Bust! Richard Feynman’s Last Journey, by Ralph Leighton

The Road to Tannu Tuva is the realization that the rigor of doubt and uncertainty on the one hand, and the joy of discovery on the other, need not be strangers. Far more than that, it is the realization that they are and ought to be considered one and the same.

Although truth be told, around these parts we have a different expression for this idea:

Clear Eyes. Full Hearts.


We Had The Same Crazy Idea


“Not everyone can become a great artist, but a great artist can come from anywhere.”

Anton Ego, from Ratatouille (2007)

“One look and I knew we had the same crazy idea.”

Remy, from Ratatouille (2007)

No narrative talk today. I want to talk about something else.

There is a moment – a very special moment – in the life of every asset allocator or financial advisor. Maybe it happens at a dinner with a fund manager, and that pre-dinner cocktail and the second glass of wine are starting to make some magic happen. Maybe it happens in a planning session for the next year, or in a marathon strategic asset allocation roundtable, looking for ideas to fill the gap on expected returns. Maybe it’s after hearing a brilliant peer speak about it at a conference at the Breakers. But sooner or later, if you stick around this industry long enough, it will happen to you, too.

You’ll convince yourself that it’s time to start a best ideas portfolio.

During my time at Texas Teachers, one of the most common topics of conversation was this holy grail: how do we get more out of our big third-party relationships? Sure, like you’d probably guess, ‘more’ often meant better aligned fee structures. We were pretty good at getting those. Sometimes it meant access. That was easy, too. But the real holy grail, that elusive thing you never quite achieve that’s really about the friends you made along the way? Information.

The standard thinking goes like this: we are paying millions in fees to the smartest, most successful investment firms in the world. Why can’t we leverage the information we get from them and how they manage our money to improve our internal strategic and tactical asset allocation, and maybe even some of our internally managed security selection portfolios? There are parts of this thesis that are, in fact, feasible – and a pretty effective use of resources. Most of those good parts are on the asset allocation side. But the really sexy, interesting notion to most investors isn’t about improving asset allocation methodologies. It’s the idea that we as asset owners – or financial advisers – can comb through the positioning and trades of our external managers, decompose and map out what some of them are good and bad at, and build directly implemented portfolios of best ideas.

It almost never works.

I’m sorry. Leaving aside even the most basic criticisms of the difficulty of identifying sources of alpha, the problems native to this kind of analysis are many.  

The first thing you are likely to find if (and when) this particular bug bites you is that neither absolute nor benchmark-relative position weightings are a persistent indicator of their contributions to portfolio alpha. In other words, you’re going to look how you would have done if you just bought all of your best managers’ biggest positions, and wonder why it looks so lousy. I have developed hypotheses on manager sizing skill across hundreds of long-only and long/short managers, and haven’t been able to prove out those hypotheses out-of-sample in more than a handful of cases. This is not always a bug. Both absolute and relative position sizes are often a reflection of risk management or confidence in a non-negative outcome in a position, rather than an expression of a higher expected return. In my experience, big positions are usually portfolio ballast, and the names that drive whatever alpha might exist are unexpected positions of small-to-medium size.

The second thing you are likely to find is that sector-related security selection skill is painfully inconsistent. When we discover that we can’t build a portfolio out of our managers’ biggest absolute or active positions, the next step is usually to try to identify what they’re good at. We know that Manager X has done quite well historically – and has a good analyst or PM – in this sector, and that Manager Y has done quite well in this sector. What if we simply built a Frankenstein’s monster out of the positions from our best funds in each sector? There are invariably two problems with this: liquid markets manager skill within sectors is more inconsistent than most investors believe, but more importantly, positioning almost always incorporates portfolio construction necessities of the rest of the portfolio you’re ignoring. Except for dedicated sector funds, you can rarely know that the positions a manager has are an unbiased expression of their conviction since both holdings and size will often reflect the risk profile of names in other sectors within that portfolio.

The third thing you are likely to find – and this is the thing which usually blows up the beautiful dream for most investors – is that fund managers themselves rarely really know what their best ideas are. It is a truth, I think, that not every trade can be a great one, but that a great trade can come from anywhere in a well-managed portfolio. If you haven’t been going through the process of developing a sense for this among your portfolio managers over long periods of time, consider doing so.

It’s also useful for reminding yourself of the very frustrating truth that good investors don’t always know why they’re good.

This last point is of particular importance for the most common implementer of “Best Ideas Portfolios” – the financial advisors and individuals who meet with PMs or wholesalers with an aim to find 2 or 3 individual stocks they might put into a client’s portfolio because they are that manager’s best ideas. No matter what they tell you, I’m telling you: they don’t know.

Now, maybe you’ve had some historical success doing this. Even if that’s the case, I’d ask you to take a really long look at the evidence behind what you’re doing, and your confidence in the analysis of what your managers are good and bad at. And yes, with any such rule, there are going to be exceptions. As it happens, I have one in mind, too.

But that’s fodder for an upcoming longer form note, and unlike this one, it will have everything to do with narrative.


In the Flow – Beating the Recession Drums


Beating the Recession Drums

It’s too early to pick up on a Quid map, but this past week saw a noticeable pick-up in sell-side and mainstream financial media drum-beating on increased signs of an imminent recession. This “Winter Is Coming” recession narrative has been building for weeks, originally as part of the concerted Wall Street narrative effort to get Jay Powell and the Fed to stop hiking interest rates (see our Brief here), and more recently merged into the US-China game of Chicken narrative that has dominated financial media since Trump announced a 90-day ticking clock after the G20 meetings (see our Note here). All of this effort culminated in the narrative surrounding Friday’s sharp market sell-off.

Why were major US indices down >2% on Friday?

Because “weak data” shows China is heading into a “trade war” recession, and Europe and the US are next.

That’s the financial media story, and they’re sticking to it.

As always, I have no idea what the recession Truth with a capital T might be. But I do know the narrative. And in the past week I saw more of what I call “recession signal” stories than I have in a long time. Stories like housing prices rolling over in Las Vegas.

Stories like fewer people buying business suits at Men’s Wearhouse. Stories like sub-sub-prime auto financing running rampant. Stories like this company or that company maybe taking down 2019 numbers. All anecdotal, for sure, but ring-of-truth stories like this are the mother’s milk of a growing narrative. Put this on top of “yield curve mania”, where everyone and his brother is writing a story about the last time that 2s/5s inverted by 3 bps and the inevitable recession that follows … it’s a strong effort, for sure.

And it wasn’t limited to US financial press. The biggest news event for markets this week was the ho-hum ECB meeting, where Draghi et al confirmed plans to stop purchasing bonds to expand the ECB’s balance sheet, and coupled that with the usual uber-dovish song and dance at the press conference. I say ho-hum, because the European financial press is clearly bored to tears with the whole act. To paraphrase the narrative here, Draghi is now just phoning it in. He can’t even be bothered to say anything new for a pull-quote, it’s just the same old stuff about needing some sort of fiscal union and about how QE is “in the toolbox forever” and blah, blah, blah.

Here’s the problem for Europe. Everyone believes that everyone believes the economy is slowing (recession drum-beating and common knowledge creation) and everyone believes that no one believes that Draghi has a solution!

As a result, German bunds are now negative-yielding through 7.5 years. That’s just awful for any Europe-limited investor or allocator if we are, in fact, headed into a recession, because it really limits the diversification potential of a portfolio. Can these bonds go even further into negative territory, thus increasing in value? Sure. But they just can’t go up in price enough to counteract the hit that your equity holdings are going to take in any sort of recession scenario, particularly if the ECB is no longer going to be the buyer of last resort for these abominations.

Source: Bloomberg, Holger Zschaepitz

So that all sounds pretty awful for markets, right?

Oddly enough, I think there’s a very bullish scenario that can emerge this week.

If Jay Powell announces a dovish hike this Wednesday, where they raise rates a quarter point as expected but say something like “we’re taking a wait-and-see attitude on future hikes because we’re concerned about the impact of US-China trade disputes”, then everything is wrapped up in a neat little bow for Mr. Market.

The difference between Powell doing a dovish song and dance routine and Draghi doing a dovish song and dance routine is two-fold. First, it will change people’s minds about previously hawkish, taciturn Powell, and that’s what defines informational power – how much does it shock you? Second, and this is where words matter a lot, a bullish narrative requires that Powell mention US-China trade disputes. If the narrative becomes that there’s a Powell put beneath a China trade war … well, that changes everything.

To be sure, if this isn’t the story that comes out of the Fed presser on Wednesday afternoon, then the rest of the year is looking mighty, mighty lump of coal-ish. But I think it’s the most likely narrative to emerge. Taking a flyer on Powell saying all the right things on Wednesday is a very focused trade with a nicely asymmetric risk/reward framework. More broadly, this is the sort of event that has the potential to change the market narrative around US-China trade, where, to date, the Fed has not been an explicit player. Will Powell take this opportunity to insert himself into the mix? If no, then we’re back to all Trump-Xi Chicken all the time. If yes, that would be a highly market-supportive move, at least over the next few months. Should be an interesting week! 

PDF Download: In Summary Dec. 8 – Dec. 14, 2018


Sin Boldly


But the fruit of the Spirit is love, joy, peace, longsuffering, kindness, goodness, faithfulness, gentleness, self-control. Against such there is no law.

The Bible, Galatians 5:23

God does not save those who are only imaginary sinners. Be a sinner, and sin boldly.

Martin Luther in a Letter to Philip Melancthon (1521)

I have a 2- and a 4-year old. They have taught me to revere lessons of self-control.

Now, there were three things my lifelong Texan wife and I knew we had to do immediately when we moved to Connecticut. We had to find a grocery store that sold Shiner Bock, we had to find a butcher who sold full packer cut briskets, and we had to find a restaurant that sold Tex-Mex style queso and another that sold chicken fried steak. Of all these tasks, the last was the hardest. You may not be familiar with the dish. Chicken fried steak – called ‘country-fried steak’ in places that make terrible chicken fried steak – is a steak that has been pounded flat and thin, dredged in a flour breading and pan- or deep-fried.  It’s unclear whether its origin is more southern (in keeping with the name) and related to a very similar process for breading and frying chicken, or more related to the very similar schnitzel dishes that the many German immigrants to Central Texas would have brought with them. My money is on the latter. Chicken fried steak is basically Wiener Schnitzel, except that it is made from beef from cows a year or two older, usually without breadcrumbs in the breading, and with cream-and-pepper gravy instead of lemons and parsley. My undying thanks go to Adolphe, Grand Duke of Luxembourg, who sponsored the Verein zum Schutz(e) Deutscher Einwanderer in Texas, better known as the Mainzer Adelsverein, for the greatest unanticipated result of his patronage.

The best chicken fried steak I ever had was at a place called RO’s Outpost, a mediocre, now-closed BBQ joint in Spicewood, Texas that also pounded, breaded and fried an extraordinary steak to-order. Other very passable fresh preparations are available in places like Goodson’s in Tomball, Texas. But in most cases – even in Texas and the south – you’re going to get a frozen product distributed by Sysco. Maybe by PFG or US Foods. Sadly, your best bet is finding a restaurant that does the most consistent job of frying the flour-dredged, ‘naturally shaped’ version of a frozen steak. Sysco calls theirs the Angus Country Fried Steak. The most consistent version used to be at Chili’s, believe it or not, before most locations took it off the menu. The worst? An unnaturally shaped, low-end version served by the pseudosouthern Moxie and Charleston Chew sign-bedecked hellscape that is Cracker Barrel. Your best nationwide bet these days is at Louisville, Kentucky-based Texas Roadhouse.

So it happened that I took the family to a nearby Texas Roadhouse location, where my two sons decided to regale the restaurant with their newest trick, which is to combine toilet humor silliness with catchy songs. Their choice for all the other restaurant guests that day was the theme song to Mr. Rogers’ Neighborhood, but one that replaced the word “neighbor” with a different, funnier word. Their flagship variant was “would you be mine, could you be mine, won’t you be my poopoo,” but there were as many others as there are two-syllable words for body parts or bodily functions.

So yes, even though I spent three paragraphs talking about fried ribeye steaks smothered in gravy, I AM, like every other exhausted parent of toddlers, a believer in the lessons of self-control. Or at least in the idea that it will, someday, emerge.

This goes double for my views as an investor.

I have written (in language stolen from Friedman) that investment returns are always and everywhere a behavioral phenomenon. There is very little that the average or professional investor can do to improve their thinking more than by becoming students of behavior, psychology and evolutionary biology. Some of those benefits may be gained by exploiting the recurring and predictable tendencies of other humans, although it is a thing much easier said than done. I still think that the greatest – and certainly most attainable – gains are to be had through awareness and command of our own tendencies. Through self-control.

But there’s a problem with this idea: self-control, at least as it manifests in our brains, is an imperfect tool for overcoming behavioral biases.

Most of self-control as we think of it works through mental models. We synthesize a lot of different ideas, concepts and experiences to build an abstracted idea of what acceptable behavior looks like. Then we (mostly) consciously enforce it on ourselves. Most of those concepts are moral standards or cultural norms. Limit your gluttonous consumption of fried beef, on the one hand, and don’t stand up in your chair at Texas Roadhouse to sing songs featuring your sexual organs at the top of your lungs, on the other. Reasonable enough. As investors, it is easy and helpful enough to incorporate aphorisms and heuristics into this conscious management of our own behavior. Be fearful when others are greedy, don’t play for lottery outcomes, be a seller of insurance after a hurricane, that kind of thing.

The problem is that the same mental models we create to self-audit and control our behavior are exactly the ones on which narratives work. The language of common knowledge is the language of normative thoughts.

When we write about the nudging oligarchy, we write about those with influence over common knowledge, who exert small pushes on our framework toward what they believe to be right behavior. Political and patriotic correctness are forms of this, too, seeking to exert explicit influence on the cultural norms we incorporate into our baseline to which acts of self-control would return us. Many narratives are just repurposed versions of ancient memes, and are themselves sources of behavior to be controlled by our awareness and capacity for self-control. But just as many seek to tell you how to think about what it means to be a good person. A positive social influence. A real investor. A real financial advisor. A real Graham & Dodd adherent or student of Buffett. These aren’t behavioral biases. They are attempts to change the baseline against which you measure behavioral deviation!

I’m not saying all those things are bad or that all the intentions behind those promoting them are evil. We are all trying to influence one another, after all. But if we, as investors, rely only on our self-control and discipline to navigate biases, even if we succeed (and we won’t), we remain susceptible to those who would change seek to change our measuring sticks.

There’s only one way around this tendency: we must be explicit in stating our principles, and we must convert those principles into process.

I think there’s another powerful benefit to this as well: the protection of a process to constrain bias allows our conscious thoughts to be much less constrained by the abstractions of socially constructed norms about investing. We are free to think dumb ideas, irrational-sounding ideas, absurd-seeming ideas.

To sin boldly.  


Notes from the Diamond #5: Wannabes Beware


If the Lord were a pitcher, he would pitch like Pedro [Martinez]. 
 — Pro baseballer David Segui

Plagiarism is the cardinal virtue of investing.
— Pro investor Jeremy Grantham (?)

Trivia Question #5 of 108: Which of the following phenomena has occurred just once since the inception of Major League Baseball (MLB) in 1876?  When choosing among the options listed below, note that in 143 years of MLB competition, 217,082 games have been played, with well over 30 million pitches thrown during almost 15 million at-bats.

A – A player’s mother hit and injured by a fouled-off pitch thrown by her son on Mother’s Day. 
B – A spectator hit and injured by two foul balls hit by the same batter in a single plate appearance. 
C – A player killed by a pitch. 
D – All of the above.

Among the many things investing and pro baseball have in common, perhaps foremost among them is a rich tradition of purposeful mimicry: a conscious copycatting of methods that have worked well for the persons who devised them originally and that can presumably be put to good use by others, competitors not excepted.  Of course, some methods are harder to mimic than others, including the idiosyncratic approach to pitch selection and execution employed by Pedro Martinez during his Hall of Fame career, or the equally inimitable approach to capital deployment employed by my former partner and valued mentor Jeremy Grantham.  Jeremy has served up so many witty and wise tidbits about investing since my path first crossed his 35 years ago that I’ll credit him as the original utterer of the above wisecrack about our shared profession’s cardinal virtue — even if Jeremy borrowed it from someone else or, contrary to my recollection, never uttered it as quoted here. 

If indeed Jeremy borrowed the wisecrack in question from another sage, that act might be the only instance of his having engaged in plagiarism, trivial or otherwise: like select pros in both baseball and investing who are properly deemed original thinkers, Jeremy constitutes an exception proving the rule that copycatting of demonstrably successful methods is a logical means of gaining an edge in either of the two arenas on which these notes focus — baseball or investing.

The problem with copycatting, of course, is that it’s not a wholly reliable means of getting ahead in either baseball or investing — endeavors in which changing circumstances beyond a player’s control or divination can affect outcomes to a substantial degree.  ET faithful know well whereof I speak respecting uncontrollable and unforecastable forces affecting asset prices, Ben Hunt having discussed such phenomena eloquently and arrestingly in his seminal essay The Three-Body Problemand follow-on series Things Fall ApartFocusing as this note does on the varied fortunes of wannabes in baseball and investing, it necessarily discusses how chance events have helped shape such fortunes, sometimes for the better, as with arguably the most skilled capital allocator in recent decades (Yale CIO David Swensen), and sometimes for the worse, as with the persons on whom the trivia question above focuses.  Sadly, the correct answer to that question is D — All of the Above — Hall of Fame pitcher Bob Feller’s mother having been smacked by a fouled-off pitch thrown by her son on Mother’s Day 1939; Richie Ashburn of the Phialdelphia Phillies having hit spectator Alice Roth with two bone-breaking foul balls in a single at-bat in 1957; and Ray Chapman having been killed by a pitch in 1920 under circumstances worthy of contemplation by risk-takers of all kinds, including especially those of us — perhaps most of us? — inclined to mimic others’ winning ways rather than attempting to fashion our own.

Amazingly, on the very day that Chapman got beaned, a how-to manual for young baseballers was published featuring images of Chapman and other MLB stars doing things that gave each of them a presumed edge.  Chapman’s highlighted forte was a batting stance that the manual’s author (baseball writer par excellence John Sheridan) deemed “the perfect model for all baseball players in his position at the bat.” 

Alas, such athleticism was of no help to Chapman during his ill-fated final at-bat, confronted as he was by a pitcher — Carl Mays — whose “submarine” delivery made it hard for even skilled batsmen like Chapman to see let alone hit balls Mays flung toward them.  Chapman’s difficulties that fatal day in 1920 were compounded by a heavily soiled ball — an acute hazard for batters obsoleted by rules changes catalyzed by Chapman’s death, to the unending gratitude of ball purveyors enriched by MLB’s post-1920 policy of jettisoning balls showing the slightest signs of wear and tear.  Unable to see the pitch that ultimately killed him, the normally nimble Chapman remained immobile during the roughly half-second it took for the ball to travel from Mays’ hand to his unprotected skull.  (Batting helmets hadn’t yet been invented and didn’t become mandatory in MLB until 1970).

What useful lessons if any might wannabes in baseball (or investing) like me (or you) draw from Chapman’s untimely beaning?  Two such lessons come to mind — both easily synopsized and both serving as useful segues to the discussion of conscious copycatting’s perils that constitutes the remainder of this note.

The first lesson gleanable from Chapman’s final at-bat is perhaps obvious: beware changed boundary conditions that render proven methods useless at best and hazardous at worst (e.g., a darkened ball hurled in a highly unconventional way). 

The second lesson is more subtle but even more germane to the sorry and indeed ongoing saga of the sizable slice of investors who’ve tried and failed to “be like Yale”: beware the use of demonstrably successful methods bereft of their  central virtues. 

[Readers puzzled by my use of slice in the prior paragraph are hereby reminded that it’s the collective noun for a group of lemmings.] 

Until Mays’ pitch hit him, Chapman’s final at-bat didn’t differ in kind from his 3,794 prior trips to the plate, 1,053 of which resulted in hits and all of which purportedly entailed Chapman’s assumption of the “model” stance referenced earlier.  In hindsight, that stance was a necessary but insufficient condition for Chapman’s success and ultimately survival as a batsman. 

This isn’t to say that a baseball wannabe like me or indeed even the most nimble batsman might have dodged Mays’ bullets more successfully than did Chapman on August 16, 1920.  Rather, it’s to say that extraordinary success in most fields of endeavor typically presupposes proficiencies that include but go beyond those susceptible of codification and copycatting — copycatting of the sort undertaken by wannabes convinced that a given “model” will surely yield results for themselves as stellar as those achieved by the model’s initial user(s).

The point just made — the chief point of this note for sorry souls who care little about baseball but lots about investing — was not coincidentally also the chief point of a review of Swensen’s pathbreaking book on institutional investing (Pioneering Portfolio Management) that appeared in Barron’s at the time of its initial publication in 2000:

Rooted as its success is in the idiosyncratic personality of the man who fashioned it, the ‘unconventional’ money management approach that Swensen extols is anything but a guaranteed path to profit for institutions lacking such talent. Indeed, numerous fiduciaries are likely to read this book and do precisely the opposite of what Swensen advocates: commit excessive sums to market niches whose strong past performance has removed the discomfort associated with truly superior investment opportunities.  [Ed.: highlight added]

I’m certain the seer bloke who wrote the review just quoted won’t object to my highlighting of Swensen’s idiosyncracies (more on which later) — because that reviewer was me.[1]  How well did my review of Swensen’s masterwork foretell its eventual impact on institutional investing?  Fairly well, I’d argue, given the massive movement of institutional capital since the book’s publication into two forms of investing that Swensen has long favored: absolute return oriented hedge funds (AROHF) and private equity (PE).  (N.B.: As did Swensen from the start of his tenure as Yale’s CIO until recently, I’m using PE in this note to encompass investments in non-listed companies of all kinds, regardless of stage.)  For good and important reasons, Yale doesn’t disclose how individual managers it employs have performed.  But a careful review of the superb reports that Swensen’s office has published since 2000 confirms that a key tenet of the investment philosophy David devised for Yale during his opening years as its CIO and has implemented faithfully throughout his tenure in that post has proven sound: favor forms of investing entailing superabundant dispersion of returns among investment pros engaged in such activity.

Can skilled allocators like Swensen and the select band of other institutional CIOs trained by him continue booking uncommonly plump profits through savvy manager selection and sizing, especially in the ARO and PE arenas?  For reasons discussed below, I wouldn’t bet against the leader of this particular band, nor its other members, even if the challenges confronting ARO managers that Ben flagged in Three-Body Problem and the overfunding of PE strategies that Rusty flagged in Deals Are My Art Form persist.

What I would bet against is the achievement of hoped-for returns by an ever-expanding legion of Yale wannabes — investors marching along the path depicted in the middle panel of the table furnished at this note’s end.  Prepared originally for an essay by yours truly on the real Yale model and its generally ineffectual copycatting by Swensen wannabes that’s available on request, the table just referenced also talks my own walk, if you will, flagging salient features of my preferred approach to deployment of long-term capital (A Third Way) as this not unhappy century continues to unfold.

Wait: given the growing centrifugation of markets and politics that Ben chronicles so convincingly in Things Fall Apart, how can conscientious capital allocators or citizens more generally be anything but unhappy about the century now unfolding? 

A few palliatives come to mind.

The first such palliative has been and remains readily available at zero financial cost: root for the team that’s won more World Series crowns than any other this century and seems well positioned to win more.[2]

Key Tenets of The Yale Model  
•  Favor equities over bonds – ownership over creditorship
•  Favor inefficient markets – as measured by dispersion of active manager returns
•  Sacrifice liquidity – as a necessary means to the end of exploiting inefficiencies
•  Diversify — to a prudent but not excessive extent

The second such palliative has been in shorter supply than the one just described since the current century dawned, though easier to come by over the last decade than I if not also most investment pros would have predicted if pressed for a forecast as the grim holiday season of 2008 was unfolding.  The palliative in question? Stick one’s nose into the fee trough for purveyors of alternative investments![3]  Constantly replenished as this trough was via massive funding of ARO and PE managers in the decade following publication of Swensen’s book in 2000, it continues to enrich many investment pros despite headwinds for ARO investors resulting from easy money policies pursued by major central banks throughout the current decade.

The third and final palliative for discontent in these disquieting times worthy of mention here is truly scarce — scarcer indeed than the analogous remedy for like ills being pursued by a growing number of MLB teams vexed by their underperformance in recent years.  Mindful as they are that this decade’s most memorably successful teams have relied heavily on draft picks to reach the top, the wannabe teams in question are essentially sabotaging themselves in order to hasten their rebuilds. 

The problem with such a strategy or model — which has undeniably worked well for the Astros and Cubs if not also another World Series winner in recent years that modesty prevents me from identifying by name here — is that the supply of potential draftees capable of reversing an underperforming club’s fortunes is deceptively small.  Indeed, such supply is even smaller than the supply of PE wunderkinds  who’ve contributed so heavily to the Yale endowment’s success since Swensen wrote the book on picking such pros nearly two decades ago: as a careful review of the aforementioned annual reports from Swensen’s office will confirm, Yale has booked uncommonly large gains from PE investments (as well as ARHOFs) during David’s tenure as its CIO, with the lion’s share of such value-added derived from that portion of Yale’s PE program dedicated to venture investing.[4]

No one knows whether or for how long Yale will perpetuate its hugely successful record of backing winning managers, especially in private markets and most notably in the subset thereof comprising early stage investments.[5]  That said, presuming as I do that Yale trustees are wise enough to let David continue serving as Yale’s CIO for as long as he wishes, I’d take the over on Swensen’s continuing success, today’s widespread copycatting of codifiable if not also quantifiable elements of Swensen’s winning “model” notwithstanding.

Of course, the most readily quantifiable element of the Yale model as devised and implemented by Swensen is also its most inimitable: its inventor’s long and ongoing tenure as Yale’s CIO — 33 years and counting.  As a future note on “coaching trees” in baseball and investing will argue, long CIO tenures like Swensen’s typically produce “compounding” in ways and to degrees not readily apparent to persons not engaged in institutional funds management. Uncommonly long and successful tenures like Swensen’s also make plain to serious students of the methods underlying them what casual observers of such labors typically miss, namely the relentless quest for excellence in which the very best players are continuously engaged. 

I discussed the personal qualities animating Swensen’s manifest quest for excellence in the review of his book mentioned above and will return to such attributes before closing this note.  Before doing so, however, I’ll give myself and readers who fancy such jollies a holiday gift by spinning briefly parallel tales involving baseballers whose long and successful careers owed much to their own relentless quests for excellence. 

As all good and perhaps most other Americans are aware, Cal Ripken played in more consecutive games than any player in MLB history (2,632), compiling 3,184 hits in 11,551 at-bats over the course of a career in which “The Iron Man” employed 20-odd different batting stances.  As the accompanying photos hint, none of Ripken’s stances were as distinctive as that of the acknowledged king of strange stances, Kevin Youkilis.  But all of Ripken’s many stances as well as the one and only stance “Youk” used during his salad days in The Show provided a sound foundation for what batting icon Ted Williams called “the single most difficult thing to do in sport”: make solid contact with baseballs thrown by competent big league pitchers.

Cal Ripken displaying two of the 20-odd batting stances he employed during his 21-year MLB career (1981 – 2001) 
Gar “The Batting Stance Guy” Ryness (left) and the Big Leaguer he’s most enjoyed impersonating over the years: Kevin Youkilis (MLB 2004 -2013)

Tellingly, notwithstanding his own ample athleticism and masterful mimicry of star hitters’ stances in countless paid gigs over the years, Gar “The Batting Stance Guy” Ryness has never had an at-bat in a real baseball game above the high school level.  He’s never been so blessed because he’s never developed what Ripken, Youk, and other effective batsmen throughout MLB history have worked hard to hone: a repeatable process for moving their bats through the hitting zone in a manner conducive to making solid contact with baseballs thrown by big league pitchers, regardless of how factors beyond their control or indeed self-initiated changes in their pre-swing postures shape up.

If the paean just offered to getting crucial deeds done in a repeatable and effective manner reads like it was lifted from every checklist you’ve ever used seen for vetting investment pros, that’s by design.  How many such pros work as intensely and tirelessly as Ripken and Youk did during their playing careers to hone methods enabling them to perform key tasks with extraordinary effectiveness?  Not many, IMO.  How many investment pros who fill the bill just submitted have engaged in such intense honing for as many years as Ripken did after achieving fame if not also tidy fortunes?  A vanishingly small number in my opinion and experience, albeit with two pros already saluted in this note being conspicuously among them: Jeremy Grantham and David Swensen.

Leaving fun and happy tales of certain close encounters I’ve had with Jeremy for future notes, I’ll recount briefly here one such tale involving Swensen, plus a similar tale involving arguably the greatest and unarguably the most intensely competitive third baseman in MLB history.

About a dozen years ago, I accepted an invite from David to grab lunch with him following a squash match between us on Yale’s courts that included a long and crucial point that ended with a muffed shot by Swensen.  Midway through lunch, his attention drifted.  He grew silent for a spell, stared down at the table, smacked an open palm on it, and muttered unsmilingly, “Damn. I should’ve won that point, not you.”

Wannabe as successful deploying capital via coveted managers as Swensen has been and continues to be?  Study his book and the Yale endowment reports commended above all you want, mimicking to whatever extent you wish the methods Swensen has employed in his tenure as Yale’s CIO.  Absent an intolerance of mediocrity rivaling Swensen’s, I’d respectfully suggest, I doubt you’ll be as successful as he’s been, or as you wannabe.

This same intensely competitive mindset was evident the day my path crossed that of Hall of Fame third baseman Mike Schmidt, on the 18th hole of his home golf course in Florida.  As my playing partner and I were strolling up the fairway after hitting our tee shots, we were puzzled by the sight of a cart racing up the fairway toward us.  Stopping the cart about 150 yards from the 18th green, its sole occupant leapt out of the cart, dropped a golf ball on the turf, quickly took a stance with the only club he’d brought along, and proceeded to hit the ball to within “gimme” distance of the hole.  Spinning around to face my playing partner and me as we came up behind him, Schmidt growled, “I knew I had that shot.  You can keep the ball.”  Schmidt then jumped back into the cart and roared off to the clubhouse.  Upon reaching it a few minutes later, my companion and I learned that a poorly struck approach shot on the 18th had cost the baseball legend a win in a high stakes match contested as our own round was underway.

Mike Schmidt (MLB 1972 – 1989)

Small wonder that a man pursuing excellence so relentlessly made it into his chosen profession’s Hall of Fame — a just honor for a twelve-time All-Star and three-time league MVP who averaged the same number of home runs per season during his 17-year career in the big leagues (32) as Babe Ruth did during his more celebrated albeit longer (22-year) run in The Show.

I’m sending Ruth to the plate so to speak as this note draws to a close because aspects of his remarkable life underscore nicely this note’s central point, i.e., that extraordinary success in most fields of endeavor presupposes proficiencies that include but go beyond those susceptible of copycatting.  The Bambino proves this point because, in sharp contrast to his fellow Hall of Famer Schmidt, whose batting methods were truly distinctive, Ruth the batter was a copycat to the core.[6] More specifically, from his earliest days playing sandlot ball at St. Mary’s Industrial School in Baltimore in the opening years of the 20th century to the end of his storybook career in 1935, Ruth parroted precisely and faithfully the stance, swing and even pigeon-toed gait of his first mentor on the diamond and his only true mentor off it: Brother Martin “Matthias” Boutlier.

To be sure, the techniques that Ruth borrowed from Boutlier did Ruth relatively little good during the first half-dozen of his 22 seasons as a big leaguer — the closing years of MLB’s “dead ball era” during which Ruth averaged fewer than nine homers per season.  Fortunately for Ruth, and the MLB team that employed him prior to 1920, Brother Matthias had seen to it that his prized mentee at St. Mary’s had also honed top notch pitching skills — skills acquired when Boutlier shifted Ruth from outfielder to pitcher after Ruth complained about a classmate’s ineffectiveness on the mound.

To Ruth’s if not also Boutlier’s credit, when MLB’s “live ball era” commenced, Ruth had little difficulty adapting to his radically changed environment, switching back to the outfield when playing defense and applying his hard-earned baseball savvy and skills more or less solely to the task of driving in runs.  In this pursuit, he succeeded admirably, notching an astounding 1,990 RBIs (runs batted in) as a “live ball” hitter.

What would’ve come of the Bambino if more tightly wound balls hadn’t been introduced in 1920 or subsequent years?  ‘Tis hard to say, though presumably Ruth would’ve carried on as a premier pitcher until his arm … or liver … gave out.

What would’ve come of Ray Chapman if he’d done the right but potentially embarrassing thing and dropped pre-emptively and safely to the ground upon losing sight of the pitch that killed him?  ‘Tis also hard to say, though presumably Chapman would’ve gone on to compile an enviable if not Cooperstown-caliber record as a batsman if he’d lived and played through the first several years of “live ball” competition.

What would’ve come of Jeremy Grantham if he’d done the easy thing and kept the firm he’d co-founded in 1977 heavily invested in small cap stocks as they were reaching the zenith of their popularity six years later?  In the event, Jeremy did what’s he done not a few times over the course of his career: outflank the competition by replacing demonstrably winning tactics with those entailing sufficient discomfort to justify fresh funding.  Doing this in a timely and effective manner presupposes a distinctive mindset: one resembling closely if not perfectly the mindset animating David Swensen’s ongoing labors on Yale’s behalf, and the mindset of Swensen’s countless wannabes not at all.

Finally, what would’ve become of the PE industry if, instead of devoting a large fraction of Yale’s endowment to PE, David Swensen had deployed all such capital via managers investing exclusively in publicly-traded stocks of owner-operated firms?  ‘Tis hard to say, but I’ll try … in my next note.

On deck: hittin’ em where they ain’t 

PDF Download (Paid Subscription Required): Notes from the Diamond #5 – Wannabes Beware

Comments welcome on Notes from the Diamond!

Contact David directly at:
     Email: [email protected]
     Twitter: @dsaleminvestor


[1] The full review appeared in Barron’s on June 5, 2000 and is available at www.barrons.com or by contacting its author here.

[2] ET management frowns on too-frequent shout-outs to the MLB franchise in question, so I won’t mention by name here the team that’s won four of the 19 World Series held since the current century commenced, including the most recent. [ET management note: sigh.]

[3] As ET faithful know, Ben and Rusty use italicized shout-outs like alternative investments! when referencing self-sustaining memes whose acceptance tends to be both uncritical and widespread.  Private equity circa 2018 exemplifies such a meme — still “bucketed” as “alternative” by many institutional investors and consultants thereto despite its near-ubiquity in institutional asset mixes.  Hedge funds remain similarly “bucketed” in institutional investment — and might ultimately merit such labeling anew if recent trends disfavoring such vehicles render them sufficiently upopular.  Readers seeking greater understanding of italicized shout-outs by ET contributors might usefully review the note by Ben in which they first appeared.

[4] Perhaps the most time-efficient means that skeptical readers can employ to verify characterizations of Yale’s investment results proferred here would be to peruse two of the 17 annual reports on Yale’s endowment posted on that school’s website: the 2007 edition and the most recently released edition, for FY2017.  Section 5 of both reports, entitled Investment Performance, breaks out Yale’s returns by asset class, reporting such returns over various intervals, including the 10-year periods ending in the fiscal year being reported upon.  By essentially linking asset class returns for the 10-year periods discussed in Swensen’s 2007 and 2017 reports, one can confirm what all of Swensen’s putative peers have long known: that David and his team are uncommonly good at picking managers within investment niches characterized by wide dispersion in active managers’ returns.

[5] According to the Yale Endoment report for FY2017, Yale earned a dollar-weighted IRR of 106.3% and a time-weighted return of 25.5% on its venture investments over the 20 years ending June 30, 2017.

[6] Planting his feet as far from the plate as rules allowed in order to avoid being “jammed” by inside pitches, Schmidt typically would turn his back toward the pitcher and pull his butt back and forth while awaiting deliveries, thus staying loose while also distracting or at least trying to distract his enemy on the mound.


In the News | Week of 12.17.2018


In the News highlights key news stories from the prior quarter for companies announcing earnings over the next week, or for other major economic announcements. These stories are not the most read or the most important, but they are the most representative of the stories that mention these companies and events.

FOMC Meeting

Fed Weights Wait-and-See Approach on Future Rate Increases

Retreating from Trade Battle, Market Instead Eyes Weak Foreign Data

The Yield Curve Just Inverted – Sort Of – And That Is a Sell Signal For Stocks

U.S. consumer spending rises strongly, inflation moderates

Powell’s Dovish Rate Tilt Reflects Fear of Fool-in-Shower Trap

Goldman Sachs Sees Risk of Greenback Nearing a ‘Messy Top’

Oracle Corporation (ORCL)

Cloudera CEO on Hortonworks merger: People expect us to be the next Oracle

In this higher-growth market, investors are looking beyond FAANG

Zuora CEO Lauds Lack of Innovation From Rivals Oracle, SAP

Google’s new cloud chief has a culture clash ahead of him after 22 years at Oracle

‘Keep Talkin’ Larry’: Amazon Is Close to Tossing Oracle Software

Carnival Cruise Line (CCL)

U.S. News Reveals the 2019 Best Cruise Lines

Mardi Gras Selected As Name For Largest Carnival Cruise Line Ship Ever Constructed

Carnival Cruise Corporation: At The Mercy Of The Economy?

Chief Fun Officer Shaquille O’Neal To Open First ‘Big Chicken’ Restaurant At Sea Aboard Carnival Radiance

Darden Restaurants (DRI)

Former Darden Senior Executive, Dave Pickens, to Join FoodFirst Global Restaurants, Inc. as President & Chief Operations Officer

Yum Brands’ Pizza Hut is losing out on one key demographic

Activist Soup Ad is Mmmm Mmmm Good

Centerbridge to buy P.F. Chang’s for $1.1 billion

8 Restaurant Stocks Dealing With a Dining Slowdown

FedEx Corporation (FDX)

CEOs Work to Figure Out Trump — Journal Report

Walgreens and FedEx launching next-day prescription delivery service

Key Takeaways From The Blockchain In Transport Alliance Fall Symposium

UPS’s Christmas Wish: A Delivery Surge It Can Handle

Micron Technology (MU)

Favorable Samsung Comments Will Not Save Micron

Micron: Has The Market Lost Its Mind Or Have ‘We’?

Micron Collaborates with Premium German Automaker to Advance Automotive Memory Technologies

Micron Technology: The Long Term Takes A Sober Approach

Micron Joins CERN openlab, Bringing New Machine Learning Capabilities to Advance Science and Research

General Mills (GIS)

23 Huge Misconceptions About the Grocery Business, According to Its Emerging Leaders

Cheerios Inspires General Mills To Innovate Employee Experience

General Mills: We May Drop Below $40 Briefly

Are Millennials Killing Name Brands?

Cereal sales are on the decline, but that’s not stopping Funko from serving up a sugary breakfast

Paychex, Inc. (PAYX)

No items of note.

Accenture PLC (ACN)

No items of note. 

Conagra Brands (CAG)

Duncan Hines cake mixes recalled over Salmonella fears

A trio of packaging production tales that end happily in 2018

Nike Inc (NKE)

Which Players Will Benefit Most from NBA’s Sneaker Colorway Rule?

Why Balenciaga’s New Track Sneaker Is Better Than Its Triple S

17 Unforgettable – Oh, Let’s Just Come Out and Say It, Ugly! – Sneakers That Defined 2018

$1,000 sneakers? An X-ray shines light on the season’s priciest kicks

At Nike NYC, an app delivers clothes to the fitting room, and pays for what you buy

Walgreens Boots Alliance Inc. (WBA)

Walgreens takes minority stake in makeup company Birchbox. Hopes to lure customers to beauty aisles

Rural Pharmacies Are Closing: Where Does That Leave Patients?

Dividend Champion Spotlight: Walgreens Boots Alliance


Common Knowledge or Fortune?


These strange tables below come from an unusual and fiendishly tricky online trivia competition in which both Ben and I participate. The game arranges players into brackets and pits them head-to-head on a weekly basis. Everyone answers the same six questions, but importantly, each player determines how many points each question will be worth for his opponent. In other words, success in the game is not just about answering correctly, but about guessing which questions one’s opponent will be able to answer correctly. Since, as you see below, the competition keeps a record of how you answered each question, over time your relative strengths and weaknesses become apparent. Ben’s on the left. I’m on the right.

While I was matched up with and defeated Ben yesterday (bringing my all-time record against him to 2-1), he is a more difficult opponent to play against than I. It’s not because he answers 59% of questions correct to my 58%. It’s because he is far more difficult to defend. It is very hard to know which questions you ought to assign Ben few points, and which you ought to assign many, because his knowledge is more or less even across topics. Someone playing against me, on the other hand, knows not to assign me any points for a question about classical music, geography or food, and to give me all the points they can for questions about art history or television. It’s for this reason that Ben assigned me the maximum 3 points yesterday for a question about Cookie Lyon. He was unlucky, and didn’t know that my wife binged Empire on her iPad a couple months back.

These revealed holes in knowledge are also useful for telling you what you need to read and study more. For me, as you will see quite glaringly, that topic is visual art. So over the last couple months, I’ve been reading, studying and watching shows and documentaries about fine art. One of the shows I found, a British import called Fact or Fortune, captivated me. A show in which two researchers seek to establish the provenance of a disputed or ‘lost’ painting, it portrays one of the starkest examples I’ve seen of missionary power over common knowledge. The episodes play out like a good mystery, so read on only if you don’t mind having them ruined for you.

Two episodes stick out in my mind.

In the first, the trust of a great estate in the U.K. sought to establish the provenance of a painting by impressionist painter Pierre Auguste Renoir that had been hanging in the house for decades. The depth of the analysis undertaken in support of the provenance is fascinating in itself. It spans everything from an established ownership paper trail vouched for by the Bernhein-Jeune Gallery, experts in all things Renoir and Monet, to forensic analysis of the paint tints used against those in use by Renoir at the time of its supposed painting, to infrared discovery of the manufacturer’s logo on the canvas used. To the layperson like me (and to the hosts, who are admittedly invested), the outcome of the episode is shocking. At its conclusion, another authority, the Wildenstein Institute, refuses to authenticate the painting as a genuine Renoir, despite what by all appearances is overwhelming evidence. Their argument rests in part on their belief that the painting isn’t very good. This is especially problematic for the owners, as Wildenstein produces the catalogues raisonnés required by major auctionhouses as a primary and authoritative source listing authenticated works by Monet, Manet, Gauguin, Pissarro…and Renoir.

Without this imprimatur, this painting has effectively zero value.  

Another noteworthy episode tells the story of a 13th century church in a town just outside of Stoke-on-Trent, and an unidentified piece of religious art hanging from its walls. There is very little paper trail, and what exists conflicts with the church’s own stories of its provenance. After a thorough cleaning, however, the painting goes before an expert in its likely place of painting: Venice. A small group of experts evaluate various qualitative traits – the shape of one figure’s face, the way that the Christ figure’s arm hangs down, the brushstrokes – and conclude definitively: this is a work by Francesco Montemezzano.

With a fraction of the evidence of the Renoir (somewhat understandably given the vast difference in age), this painting’s effective value is now 10-15x what it would have been as an anonymous 16th century Venetian canvas.

The lessons are pretty clear:

  • Strong common knowledge exerts significant influence on how markets arrive at and establish prices.
  • Strong credentialing effects permit the existence of missionaries to influence and, at times, direct that common knowledge.

You may not agree with everything Ben and I write about the existence of narratives influencing markets. And that’s OK. We won’t be right all the time. But if your framework for understanding price-setting doesn’t involve assessment of missionaries, common knowledge and credentialing effects, you’re missing a big part of what makes a market, whether it’s a market for fine art, or a market for fractional claims on the cash flows of companies and other institutions.


In the Trenches: Bridge Out?



  • Heed the sign posts. The global economy is visibly slowing as developed market central banks have become marginally less accommodative. Emerging market (EM) economies have paid the price for higher U.S. short-rates and will continue to do so. As the ECB tapers, Europe’s structural flaws have once again become visible. U.S. housing is slowly crumbling, and large and important parts of U.S. corporate credit market are at frothy extremes.
  • A deceleration in global growth – partially driven by higher funding costs – combined with a plethora of levered IG credits (especially BBBs), a now frothy commercial and industrial (C&I) loan market, and a growing maturity wall in U.S. high yield (HY) is likely to create 2019 credit stress. This stress has challenged and will continue to challenge global equity market returns.[1]
  • While a Santa Claus will likely arrive and deliver a late-year rally for market participants holding U.S. equities, market participants ought to sell U.S. equities on strength (especially on any S&P rally above 2,800).
  • Whilst the end of the bull market is likely upon us, central banks are unlikely to ignore the signs. They will react quickly if they sense a collision is imminent. It appears that the Fed is beginning to sense the danger, and it has walked back its hawkishness in recent communications.[2]


Previously, In the Trenches stated: “It’s a matter of when rather than if – the Minsky moment is becoming more palpable. The stability caused by a decade of central bank financial suppression has led to the unintended consequence of creating a more fragile global financial system – one more vulnerable to shocks. The next shock is likely to be one of central banks’ own collective design” in the form of a harmonized withdrawal of extraordinary stimulus. [3] It bears repeating that negative real rates globally, as a product of both traditional and extraordinary monetary policy measures, have been responsible for the lack of financial asset volatility. Global central banks went to great lengths to keep the global financial system from crashing. They introduced many new safety features after the crisis, making market participants feel safer. Those include QE at-the-ready and overnight swap lines that the Fed can elect to extend to foreign counterparts and banks. Unfortunately, these new safety features have encouraged drivers to take more risk… and the drivers haven’t changed, albeit perhaps the average age is a bit younger. This has arguably created nothing more than an illusion of safety. Market participants have become even more reliant on driver-assist, and they may be prone to ignore the signs.

Caution: Hazards Ahead

So, what are these sign posts? There are at least four, the first of which we began the year sharing with anyone who would listen: capital outflows from EMs. Because U.S. rates have been on the rise, capital has flowed out of emerging markets and growth has slowed this year, and this will matter to developed markets.[4] Emerging markets currencies were the first, observable casualty of the Fed’s interet rate hikes. We simply thought they’d be weaker even earlier. We were overly bearish of emerging market currencies and equities in 2017, but our concern was realized in 2018. In 2017, we underappreciated just how titillating the synchronized global growth narrative would be for dedicated, emerging market investors looking for an excuse to pile in. In 2017, there was a critical regime shift from the usual relationship between developed market rates and EM asset performance. Figure 1’s top panel shows the positive correlation between EM equities and DM rates under the 2016 – 2017 regime versus the 2018 regime, which shows emerging market assets are once again correlating negatively to higher U.S. short rates. The latter paradigm is more often the norm. What is the catalyst for capital flows back into EM economies? With even just another two rate hikes from the Fed, EM markets should continue to experience capital outflows and pressure on their currencies as other developed market central banks continue the normalization experiment. The smaller the EM the more sensitive its currency will tend to be to higher developed market rates; weaker currencies force EM central banks to raise rates to prevent inflation. In turn, growth slows.

Figure 1: EEM Performance versus U.S. 2-year 2016 – 2017 (top) and 2018 (bottom); 
Source: Cantor and Bloomberg HRA

Second, Europe’s structural governance issues are becoming more visible in light of a less accomodative ECB. The European Union has structural flaws related to the monetary-only nature of the union. The necessary move towards a fiscal union (or at least fear of it) is resulting in the spread of populism –even France is not immune. Moreover, even the monetary union itself has a number of chinks is its armour. In particular, all sovereign debt issued within the union – regardless of country of issue – receives the same collateral treatment within the each country’s banking system. The result has been a mispricing of some European severeign debt. Only recently, for example, have Italian BTP spreads begun to widen relative to Bunds. The current tussle between the Italian government and Brussels is the beginning of an existential struggle to maintain the dream of a unified Europe. The ECB faces a hobson’s choice this week as it decides whether or not to end QE. If it does so, it should likely lavish the communication with dovish overtones. If not, who is likley to buy the €300 billion in Italy’s 2019 maturing debt? Its decision comes at the same time the Eurozone economy slows appreciably, as shown in Figure 2. The European statistics agency lowered growth for the third quarter from .7% to .6%. Could it be possible the Fed is walking back its hawkishness in recent weeks to give the ECB some room to end QE despite the weakening economic data?

Figure 2: European PMIs Have Rolled Over

The third signpost is U.S. housing. The median income houshold can no longer afford to buy a home, per Cantor’s proprietary home affordability indicator. In part, this has been caused by artificially low inventories of existing homes caused by homes removed from inventory by HARP refinancings. HARP refinancers are effectively living in subsidized homes they would not otherwise be able to afford, and thus, they have little incentive to sell. An artificial dearth of supply has led to higher prices. As usual, unintended consequences of intervention ultimately present themsleves. Of course, it had also been caused by years of ultra-low interest rates, which have now begun to rise. The 30-year mortgage rate has risen from under 3.5% to recently just under 5%, increasing housing expenses for new entrants by almost 43%. The combination of extended and amended mortage obligations for 3.3 million homeowners and low long-rates facilitated by prolonged QE have produced the illusion that consumers are unlevered. With homeownership rates still stuck below historical averages at ~64%, rent obligations are not captured by debt-to-disposable income statistics. Lastly, consumer sentiment and spending has been unsupported by real wage growth, which has hovered around only 1%, well below post-recession averages. Instead, access to debt (outside the mortgage market) has driven spending and sentiment. This is likely unsustainable.

The final signpost is ‘the bubble’ we see in U.S. corporate credit in the form of corporate commercial and industrial (C&I) loans. Not only have banks aggressively lent to corporations, but companies have also issued record amounts of low-investment grade debt to investors hungry for duration and yield. Sovereign wealth funds (SWFs), which used to buy risk-free U.S. debt, have generally shifted to corporate loans and bonds as they have searched for yield. With the U.S. 2-year yield approaching 3%, and return bogies for many funds in that vicinity, we might expect a rotation out of corporates and equities and into less risky substitutes that represent better risk-adjusted return opportunities. U.S corporate debt-to-GDP is at an extreme that for the past three economic cycles has always resulted in a corporate spread widening, an increase in equity market volatility and ultimately recession. Figure 3 shows this relationship.

Figure 3: Default Rates Spike When Corporate Debt-to-GDP Exceeds 44%;
Source: Cantor, Bloomberg and S&P

Central Banks See the Signs

Central banks won’t sit idly by while the global economy careens off the road. Not unlike the safety systems of the modern automobile, central banks have become more sophisticated when it comes to collision avoidance.  In early October, Fed Chairman Powell confirmed his optimism on the U.S. economy during his interview with PBS’s Judy Woodruff when he said that “there’s no reason to think this cycle can’t continue for quite some time, effectively indefinitely.” He characterized policy as a “long way from neutral,” even based on what is now an unnaturally low neutral rate. Of late, he has seemingly dialed back his optimism. In our previous note, we felt the Fed would likely to stay the course and raise rates relatively aggressively into 2019 based on domestic economic health – largely fueled by the impact of misguided and temporary fiscal policy – at exactly the same time the rest of the world slows. Recent risk-asset voaltility has proven this concern warranted, but we’re less sure now that the Fed will continue to plod on with normalization. As Chairman Powell’s statement at his recent appearance at the Economic Club of New York suggested, rates were ‘just under neutral,’ walking back some of his previously hawkish statements.

Nonetheless, the cat is out of the bag when it comes to developed markets’ central bank policy normalization. In response, EM central banks have been forced to tighten to control capital outflows. Moreover, as we often point out, global central bank balance sheet growth has stalled in dollar terms and is now flat on the year. As we wrote previously: “Market participants have been lulled to sleep by ‘fake news’ based on temporary U.S. fiscal stimulus and the strong U.S. data it is producing. Like a bodybuilder on the wrong side of a steroid cycle, the U.S. economy will struggle if Congress exercises even a modicum of fiscal restraint.  When U.S. consumers feel this good, it’s rarely a good thing.” Whilst the end of the bull market is likely upon us due to the impact of past and current policy normalization, central banks are unlikely to ignore the signs. They will react quickly if they sense a collision is imminent. It appears that the Fed is beginning to sense the danger, and it has walked back its hawkishness in recent communications. Look to the ECB on December 13th as the next bridge global risk assets must cross. We doubt the bridge will be closed, but we are cautious on the overall outcome given the slowdown in Europe and the implications ECB action has as the marginal supplier of global liquidity.

[1]  We began 2018 with an understanding that it would be a far more volatile year, yet we felt it was a bit too early to call the end of the cycle. Thus, we maintained a modestly bullish disposition of the S&P 500. As global central banks became marginally less accommodative, our S&P target has been well-below consensus for most of the year at 2,805.

[2]  Our view is for a hike this week and for only one hike for all of 2019 (in the first half).

[3]  We ended the note by saying that “with the S&P touching 2,600 today, we’d suggest it’s time for a rally, as funds are forced to chase returns into year end. But, make no mistake, this is now a sell-the-rally market.”

[4]  Even with modest capital inflows in November, net foreign capital outflows are on track to be the largest since the GFC. With one month left in 2018, year-to-date outflows totaled $US26.1 billion. Read more at  https://www.businessinsider.com.au/emerging-market-stocks-bonds-capital-outflows-gfc-2018-12#4zmLyOD8mV2pm9RD.99.

PDF Download (Paid Subscription Required): In the Trenches: Bridge Out?

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Sources: Cantor Fitzgerald & Co. and Bloomberg


In Focus: Tesla (12/2018)


Download In Focus PDF HERE.

Sentiment: Positive and Improving

Attention: Weak and Declining

Current Narrative Map Highlights

Source: Quid, Epsilon Theory

Current Sentiment and Attention

Source: Quid, Epsilon Theory


In our broader piece on trend-following (Figaro), we noted that narratives for Tesla had demonstrated moderate-to-high attention (i.e. internal consistency) and very negative sentiment for much of the second half of 2017 into 2018. The causes for this were many, but the most interconnected negative clusters concerned (1) continued underperformance  on production numbers, (2) Elon Musk’s unusual behavior, especially on social media and (3) a number of worrisome key departures in areas that implied potential fraud. We felt that this was a supportive environment for negative trending.

We also noted that if Tesla were to break these narratives, it would be by reinforcing the positively received areas of the narrative (esp. Gigafactory production and Asia) , and by delivering a professional earnings call that might relax the frantic “management problems” components of the strong narrative.

Did they succeed? Yes, in part, we think they did. The aggregate sentiment around Tesla has improved dramatically, but the cohesiveness of the narrative strikes us as still being very mixed, similar to this time last year. We would be less convinced of betting on recent price behaviors as part of a long-term trend until there is more resolution on the negative components of the narrative.

In the medium term (and in addition to whatever long or short thesis we had), as investors in TSLA we would focus most on:

  • How market appears to be attaching Tesla to tariff and China issue. This is becoming central to the main narrative and has deeply negative sentiment.
  • Perception of CEO behavior (“single digits weeks away” is still a popular n-gram across articles)

PDF Download: Tesla (12/2018)


Basically a Snake Don’t Have Parts


Coach Klein: That looks delicious. What part do you think I’m about to eat?

Mama: Well…basically…a snake don’t have parts. But, um, if I had to call it anything…I would say it’s his knee.

Coach Klein: Great, great. And what are we having for dessert?

<A bug zapper flashes and sizzles with new prey, just outside the window>

Mama: Squirrel.

The Waterboy (1998)

Narrator: And here, in a cave about 2 million years ago, the first artist was born.

[An artist puts the finishing touches on what looks like the (much more recent) cave paintings in Lascaux, France]

And, of course, with the birth of the artist, came the inevitable afterbirth…the critic.

[The critic proceeds to urinate on the cave art]

History of the World, Part I (1981)

It is the season of snark.

The end of any year is usually when strategists and investors begin publishing next year’s outlook pieces, typically with all sorts of predictions for financial markets. You know the drill: the S&P will be this or that on December 31, 2019, and the 10-year will be here or there.  I think there’s a 40% chance of this, and while I’m not predicting a recession next year, I think it may be in our future for 2020! It’s a racket, with pre-defined rules and few real consequences. If you guess right, you take a victory lap. If you guess wrong, you move on quickly and people forget about it.

It is also the time of year for the strategist’s inevitable afterbirth: the critic. Newspaper articles and other competing strategists comb through all the predictions, looking for those most worthy of ridicule. Some are sufficiently close to parody that the critic’s work is mostly done for him (think John McAfee’s unappetizing 2020 dinner plans), while in other cases, the predictor is respected enough that the criticism is usually leavened with hushed, respectful tones (e.g. Byron Wien). Prediction criticism is a racket, too, and it also follows a similarly predictable set of rules.

Why are they rackets? Because both ridiculously overconfident predictions and their unnecessarily on-the-nose criticisms are pure advertisements, bereft of any useful information. Advisers and institutional investors must tell their charges something as the year closes out, and the only two choices are (1) here’s what we think will happen, or (2) here’s why we think nobody knows what will happen. It’s all about whether you’re selling confidence or likability. Naturally, I happen to favor the latter, because it suits my temperament and because I think it’s usually a hell of a lot more accurate, but both of these are still shtick.

The most predictable shtick these days, however, is the environment for active management shtick – as in, “This is setting up to be a good environment for active management.”

This is a very stupid idea. But the typical criticisms for it are also very stupid.

Let’s unpack them both.

First, consider that any reason given in defense of the vaunted better environment for active management will inevitably take the form of one of these three ideas:

  • There will be more volatility in markets and dispersion among stocks;
  • Forces causing markets to rise and fall in unison (e.g. central banks) will relax; or
  • Information disperses more slowly in this market, creating inefficiencies to exploit.

Please, for all of our sakes, if you’re going to play the variant of the above three rules that we call the “take a drink every time you read or hear ‘the fundamentals are starting to matter’ game” this holiday season, make sure you Uber or find a designated driver.

Fortunately, all this nonsense is easy pickins’ for the critic, who observes dryly that even if these above three states were to exist, alpha would remain a zero sum game, and that increased dispersion would simply cause the transmission mechanism between active share and active risk to rise. In other words, none of this changes whether active management will work better or worse on average, it just widens the gap between the winners and losers.

That’s obvious enough, I think? Except this idea, too, is right in all the ways that don’t matter and wrong in all the ways that do.  

Yes, yes, the market is zero sum and all that. But after she interviews a hundred fund managers, and only finds one or two that are actually overweight Apple or Microsoft, any realistic assessor of a public markets asset class will quickly come to the conclusion that the universes of active managers we most often refer to are not a reflection of the market capitalization weighted definition of that asset class. If you added up every position held by every US Large Cap mutual fund and separately managed account in the world, the portfolio you ended up with would look very different from the S&P 500.

Why? Because there are huge pools of unbenchmarked assets which would be included in a formal or academic definition of “active management”, but which exist outside of any practical definition of the universes that any asset allocator would encounter, like the actual funds, commingled funds, SMA pools and hedge funds that they can actually invest in.

These other pools are snake-and-a-squirrel portfolios, and they exist everywhere. These are not people or institutions sitting around matching what they own with a “US Mid Cap Growth” mandate. They are the holdings of wealthy individuals and restricted stock-compensated executives. They are the custom unbenchmarked (or poorly benchmarked) multi-asset income portfolios built by consultants and FAs. They are the one-off holdings of corporations, partnerships, banks and other institutions. They are the holdings of foreign investors who want to hold US stocks, but for whom that means buying the well-known megacap multinationals. And no matter how much we want Kathy Bates to tell us a comfortable story about how they’d fit into our style boxes and asset classes, they won’t. That’s why alpha is absolutely a zero-sum game in academic space, but is absolutely not a zero-sum game in any practical definition of our industry-related constructs of investable asset classes and products. What we invest in isn’t a set of strategies choosing to underweight or overweight the stocks in the S&P 500, but a set of strategies that invest in what’s left over after mama has served up a few hundred billion dollars worth of snake and a squirrel. 

The reality, then, is that there absolutely are good and bad environments for outperformance of the average fund in different asset classes, but they have nothing to do with pedantic zero-sum game arguments OR security-level dispersion. If you want heuristics for what an “active management environment” looks like, it’s this:

  • Your actively managed portfolio will usually be underweight the defining traits of the index you have selected.
    • It will be less fully invested (i.e. it will hold more cash).
    • It will usually hold less of the market cap range in question (i.e. large cap will underweight large cap, small cap will underweight small cap).
    • It will usually hold less of the largest country weight.
    • It will usually hold less of the largest sector weight.
    • It will usually have a less pronounced bet on any factor (e.g. value) used to define your index.
  • Your actively managed portfolio will usually be overweight volatility – not in the “long vol” sense we use to talk about benefiting from market volatility, but in the sense that your portfolios will tend to own more volatile stocks than your index. This is usually because most stock-pickers seek out stocks with more idiosyncratic risk, which (surprise) happens to be positively correlated with outright stock price volatility.

Don’t believe me? If you’re an FA, go through your fund lineup, approved list or fund database, and count off the funds who are overweight Apple and Microsoft. Count off the EM options you have available that are overweight China. Show me the small cap funds that beat the Russell 2000 when the Russell 2000 is roaring. They’re going to be rare.

Most of these traits and biases, of course, don’t have positive long-term expected returns. So all this does little to change the fact that active management is a brutally difficult game over long periods, especially after fees. But if you want to understand the reality underneath the abstractions and narratives being promoted in media and by your managers about active management vs. passive management, you’ve got to understand that the “environments for active management” are driven not by those stories you’re being told, and not by the skeptically dismissive arguments of the skeptic, but by these almost universal biases.