Metastability

My latest Epsilon Theory note is about the metastability of social systems.

A social system remains metastable as long as there is a reasonably broad consensus regarding its core values and mythology. Without this consensus, metastability weakens. Put another way: first-order threats to social stability, such as isolated riots and street crime, are risks that lie in the body of the distribution of outcomes, both for individuals and society. Metainstability is a higher-order threat. The risks associated with metainstability lie in the tails of the distribution. They fall under the broad category heading of Really Bad Stuff and include things like:

violent revolution

war

property expropriation

Back to the Ants and the Grasshopper. Would it behoove the Ants to share a bit of food with the other insects to shore up the metastability of the forest’s social system?

You can read the whole thing at Epsilon Theory.

Metastability is a rich concept to explore. I didn’t spend a lot of time defining metastability in my ET piece, but I find it worthwhile to look at the concept through the lens of elementary calculus.

If you’re reading this blog, you’re probably familiar with the differentiation of the simple quadratic function f(x) = x^2. The first derivative (a.k.a “instantaneous rate of change”) of f(x) = X^2 is 2x. The second derivative of f(x) = x^2 is just the derivative of 2x, the constant, 2. This, in turn, can be interpreted as the “instantaneous rate of change” for the function f(x) = 2x.

So you can see there’s some mathematical intuition behind that old saw, “change is the only constant.” It’s rates of change all the way down.

These concepts show up in finance all the time. In fixed income, there’s an inverse relationship between bond prices and yields. The first derivative of this function is a bond’s duration. The second derivative is its convexity.

With an option, the payoff depends on the price of the underlying relative to the strike price at expiration. The sensitivity of the option’s price to changes in the price of the underlying is the first derivative of this relationship. This is the option’s delta. The second derivative of this relationship, the sensitivity of the option’s delta to changes in the price of the underlying, is the option’s gamma.

(homework: consider the CAPM or any other linear factor model of financial asset returns in this context)

Anyway, on to metastability.

Take a society at any given point in time.

Social stability is its first derivative. Social stability is the instantaneous rate of change for society’s consensus values and norms.

Metastability is the second derivative. Metastability is the rate of acceleration (or deceleration) of changes in social stability.

In the language of options traders, social stability is society’s delta. Metastability is society’s gamma. Unfortunately for society, it’s generally short gamma. Which is just a fancy way of saying change is dangerous. Change stresses human social systems. The greater the magnitude of social change, and the faster the rate of change accelerates, the greater the stress on the existing social order.

Want to destroy social order in a hurry?

Lose a big war. That typically gets the job done.

Of course, this also invites the question, how would you strengthen social metastability?

By cultivating shared values and mythology.

The most common negative responses to my ET piece were comments along the lines of “the ants shouldn’t have to ‘share’/the Grasshopper should have to ‘earn’.” That’s a fine point of view. But it’s only a first-order look at the issue. Heck, from a first-order perspective, I completely agree. But that says nothing about metastability. I wish I’d made this a bit more explicit in the original post, but I did elaborate in the comments.

Actually, as far as metastability is concerned, in the fable’s base case involving the ants and a single grasshopper, it’s perfectly fine to just let the grasshopper starve. A moral philosopher might challenge that view, but the moral philosophy of this is a whole other issue.

In fact, you can easily imagine the Ayn Rand version of my “extended edition,” where all the insects are strict utilitarians. Here there’d be no need for any “metastability insurance” because of a strong consensus around libertarian utilitarian values as the organizing principles for society.

Likewise, you can imagine a Scandinavian “extended edition” where all the insects are social democrats or whatever. That society may have a very different set of consensus values and an entirely different level of metastability.

This is what I’m driving at when writing about metastability as a reflexive process, and why the social contract is necessarily something that’s negotiated. The obnoxious, twenty-five cent word for this process would be “dialectic.” Outside of relatively small, culturally homogenous communities, it becomes increasingly difficult to establish a strong consensus around values. The example of Prussia used in the post is a prime example. The Prussian “solution” to the problem of forging consensus around shared values at scale was to bind cultural identity to the state. It worked pretty well. Too well, in fact.

Anyway, for the purposes of this post I’m not concerned at all with whether libertarian or social democratic values are inherently superior. I’m more concerned with the idea that at the scale of a large, technologically advanced nation-state, maintaining social metastability is a balancing act across different constituencies.

I think I will likely have more to say on this subject in future posts.

The Eye of the Beholder

Securities represent different things to different people over different time horizons.

Over very long time horizons, common stocks represent residual claims on assets and cash flow and will trade accordingly.

Over different time horizons, common stocks can represent other things, and their “meaning” will vary across market participants. Sometimes a stock is a correlation. Sometimes it’s an industry exposure. Sometimes it’s liquidity (or the absence of liquidity).

You’ll often hear traders, analysts and portfolio managers say “such-and-such trades as a whatsit.” What they’re talking about is the dominant meaning of the security in the minds of market participants at a particular point in time.

For fundamental investors, valuation multiples are straightforward examples. They’re quantitative markers of meaning. Embedded in every valuation multiple are assumptions about a business. Everyone reading this post is probably familiar with the price/earnings ratio. Like all multiples, a “justified” version of the P/E can be constructed out of several fundamental data points.

In the case of the justified P/E, we have:

Justified P/E = Dividend Payout Ratio / (Cost of Equity – Dividend Growth Rate)

We can decompose other multiples in similar ways. Ultimately, the exercise boils down to a handful of key variables: margins; returns on capital; reinvestment needs and opportunities; a measure of opportunity cost to the investor (discount rate). Of course, a reasonably perceptive investor also realizes returns on capital are unlikely to remain static over time. You’ve got to account for the impact of competition and market saturation. How aggressively should you fade growth and profitability? The answer to that is probabilistic. It’s where qualitative judgments about a business and its management are made and then transformed into quantitative inputs.

Narrative exists at the intersection of subjective, qualitative judgments and “hard data.” Likewise, it’s at this intersection of subjective, qualitative judgments and hard data that reflexivity operates.

Aswath Damodaran does a fantastic job of recognizing this whenever he values a stock. For an example, you can look at his Lyft valuation. You can agree or disagree with his view of Lyft. What I appreciate about his approach is that it’s explicit about incorporating Narrative, and tying his quantitative assumptions to his qualitative ones.

I’m using Narrative with a capital N here because I’m not talking about spin. I’m talking about meaning. It’s easy for a reasonably competent analyst or portfolio manager to see through spin. Scammy penny stock newsletters are full of spin. Sell-side research, taken at face value, is full of spin. Spin is straightforward to test with a research process. Spin is amenable to number crunching. Developing the vision to see through spin is table stakes in both trading and investing.

Meaning, on the other hand, is necessarily more nuanced. Meaning is reflexive. Because it’s reflexive, meaning isn’t straightforward to test with a research process. It’s Schroedinger’s Cat. The cat is both alive and dead until you look inside the box. A company is both a value play and a value trap until events run their course. A stock is both a buy and a sell until the price moves decisively in one direction or another. The trading action around every stock reflects a dynamic dialogue between buyers and sellers about meaning. Sometimes, in the case of an Herbalife or a Tesla, dialogue escalates into a shouting match. In markets as in real life, shouting matches exhibit different dynamics than measured dialogues. You trade a shouting match differently than you trade a dialogue. Particularly if you’re short.

As far as your P&L is concerned, price is the arbiter of truth. Price is the only truth that matters. For all its faults, technical analysis is spot-on in emphasizing this.

“Dead money” stocks lack meaning. They lack strong, directional Narrative. They’re neither longs nor shorts. They’re empty vessels, drifting listlessly in the markets. To “work” in either direction, a stock requires a Narrative. To borrow the language of a technician, a stock without a clear directional Narrative is a stock that’s “consolidating” or “range-bound” between strong levels of support and resistance. Of course, you can still make money off these stocks. The trick is to see them as trades rather than investments–to see your position as a bet for or against the emergence of a strong directional Narrative.

This also helps explain why well-covered, large cap stocks still exhibit significant price volatility. It’s precisely because they’re well-covered. They’re perfect vessels for Narrative. Prices don’t swing on data so much as changes in the meaning of the data.

The following conditions must be present for strong directional Narratives to emerge:

A coherent and compelling qualitative story
Quantitative data supportive of the story
A missionary (or missionaries) with credibility and reach telling the story

Together, these conditions are reflexive. They can exhibit both positive and negative feedback loops. Investment manias (dot-coms, cryptocurrency) are special cases involving especially powerful feedback loops. I am thinking of writing up a “case study” or two in the next couple of weeks to flesh this out in more concrete terms.

The Social Contract

This is a (very) quick note just to record a thought for posterity. In discussing issues of natural rights and the social contract with some friends, I landed on this description of the political process:

Modern society is the output of a long and tortured series of negotiations over how and why we should structure the social contract to limit the nastiness and brutishness of the state of nature.

Edge Over Odds

This the Kelly Criterion. It is a formula well-known to both gamblers and investors. It solves for the optimal bet size, relative to your bankroll, as a function of the probability of winning a bet and the payoff for the win. The underlying intuition is often summarized as “edge over odds.” The greater your edge, the more you should bet. For example, any time you have a 100% probability of winning, Kelly says you should bet your entire bankroll, regardless of payoff.

In investing, we often throw the word “edge” around in imprecise ways.

“What’s your edge?”

We hear this question all the time. In many cases we answer with things like “no career risk,” “longer time horizon,” and “better behavior.” These may well be competitive advantages but they are not themselves edge. At least not in the Kelly sense. In Kelly terms, you have edge to the extent the probability of winning a bet exceeds the probability of losing it. When we talk about edge, we’re talking about positive expected value.

In that sense, there is “Kelly edge” to be found in many investment strategies. Buy and hold equity investing, value investing, momentum investing. These are just a few strategies where we have pretty robust evidence supporting positive expected values over time and thus at least some degree of Kelly edge. All these strategies are potentially worth a bet.

What is considerably more controversial are the sources of the Kelly edge associated with these strategies. Because when we think about investing, as opposed to gambling, there’s a distinction to be made between the Kelly edge associated with fair odds and the Kelly edge associated with mispriced odds.

A casino is a controlled environment with set payoffs that favor the house (“house edge”). Beating the dealer is an uphill battle. Simply being able to make bets with positive expected values, however small, is the holy grail for every casino gambler.

Taking the odds in craps is a “good bet” because it offers fair odds (there is no “house edge”). The payoff fairly compensates you for the risk of the bet. Whether you ultimately win or lose the bet is the outcome of a random process.

In blackjack, the basic strategy is a “good bet” because it gets you very close to fair odds, although technically the house still has a slight edge.

Card counting in blackjack, on the other hand, is a strategy for identifying and exploiting mispriced odds.

Now, it’s more complicated in investing because investing isn’t a casino game. Financial markets aren’t controlled environments where payoffs are static and specified in advance. Investing is a game where it’s possible to make all kinds of different bets with positive expected values. Moreover, the implied odds and payoffs change on a daily basis. Here the distinction between fair odds and mispriced odds is more subtle and nuanced.

I’ve deliberately avoided using the words “alpha” and “beta” up until now. But here’s how I’m thinking about these terms in this context.*

A beta process earns returns simply as compensation for bearing risk in a fair odds bet. Buying and holding a global market cap weighted equity portfolio is an obvious example of this. But plenty of active discretionary strategies make money this way, too.

An alpha process earns returns by explicitly identifying and exploiting mispriced odds. Alpha processes are about exploiting Information (in the formal sense). I provide a specific example of this further below.

A somewhat inscrutable definition of Information that I quite like is the one from Gregory Bateson: “a difference that makes a difference.”

Do value investors make money over time by making “good bets” with positive expected values, or by identifying mispriced odds? In more academic terms: is the value premium simply fair compensation for bearing a specific type of risk? I’m not going to pretend I have the definitive answer to that question. It’s a debate that’s raged for a long time. I’m certainly not going resolve it on this blog.

My personal view on the subject is that “it depends.” Event-driven value investments such as value + catalyst trades and special situations investments are more like alpha bets. The defining characteristic is the presence of a hard catalyst, usually a corporate action. Hard catalysts, after all, are the very definition of Information. In the absence of a hard catalyst, however, buying a “quality company on sale” (something I am fond of personally) is more of a fair odds bet. A value investor may well think in terms of mispriced odds. But in the absence of Information, it’s an implicit mispricing of odds.

Incidentally, this is also where investor behavior comes back into the picture. Investor behavior is quite plausibly responsible for the historical success of systematic Value and Momentum strategies, and their persistence over time.

At the risk of overreaching, I’m going to go out on a limb and suggest most of us investors earn a greater proportion of our returns from making good bets, as compensation for bearing risk, than by exploiting Information.

Does this mean we should give up on security selection and put all our money into SPY? No. Not in the least. It is plenty difficult to distinguish whether a bet is fair and worth taking, thank you very much. Furthermore, I do believe it’s possible to outperform SPY or any other capitalization weighted index by betting smart over time. Particularly if you’re able to play in less liquid market niches with less carrying capacity and thus less appeal to larger pools of capital managed by folks with a lot of money and resources to throw at Information gathering and processing.

How do you know if you’re exploiting Information versus simply placing good bets? Here is my simple test:

Ask: Do I know for sure? If so, how?

For example, I met a muni bond trader who bought a micro issue at 60 even though there was public record of it having been called at 100. This is perhaps the single best example of an alpha trade I have ever seen in my life. It is the kind of thing that should literally never happen in a reasonably efficient market. It’s the Platonic Ideal of an alpha trade. It’s a real-life version of the old joke about the academic economist who won’t pick up the $20 bill lying on the ground in front of him because he believes people are rational actors and someone should have picked it up already.

Did the trader know for sure? Yes.

How? The issue being called was a matter of public record.

It doesn’t get much cleaner than this. And of course, examples like this one are rare.

By contrast, I had a stock in my PA go up 3x over the last two years. I was of course happy about this. It is fun to make money. I modeled the business out based on publicly available information and felt the market price reflected neither the quality of the business nor its growth prospects.

Did I know for sure? No. Not even close. I simply felt I was being fairly compensated for bearing the risk associated with the bet. But I had no Information in the formal sense–no way of knowing the odds were mispriced.

Fortunately, the P&L doesn’t distinguish between money earned by exploiting Information and money earned as compensation for bearing risk. This discussion is academic. But I sure find it fun to think about. And I do believe it’s beneficial to try to reason clearly about how and why you’re making money over time.

Why?

So you can diagnose problems and potentially make adjustments if a strategy ever stops working.

* A somewhat similar formulation of the difference between beta and alpha bets:

ET Note: The Alchemy of Narrative

I revisited some of George Soros’s writing on reflexivity over the weekend (thanks Ben Hunt!). In doing so, I realized my initial reading, years ago, had been extremely superficial. Back then, I focused on feedback loops as amplifying the usual cognitive and emotional biases we point to in investment writing. Things like confirmation bias and loss aversion and overconfidence. This reading of Soros wasn’t necessarily wrong. But it was narrow and incomplete.

When Soros writes about reflexivity, he isn’t just arguing cognitive errors made by market participants cause prices to diverge from the objective reality of the fundamentals in self-reinforcing feedback loops. He’s arguing the fundamentals are often, if not always, themselves subjective realities.

Click through to Epsilon Theory to read the whole thing.

But since you got here through the blog, you also get some bonus content. Note that if you continue reading, things will get conceptual, abstract, philosophical, and maybe a little weird. Consider yourself warned. If you’re not interested in that kind of thing you can safely skip the rest of this post.

My ET note is about subjective reality in the context of financial markets. At the very end, it alludes to the fact that reflexivity and subjective realities influence all social systems. Politics. Geopolitics. Economics. It’s all reflexive. The Big Idea is this: reflexivity is what drives the cyclicality we observe throughout history. Reflexivity is why we appear to learn from history and yet are doomed to repeat it.

Back in 2013, Venkatesh Rao of Ribbonfarm wrote what turns out to be a pretty compelling explanation of how Missionaries come to an intuitive understanding of both reflexivity and subjective reality, in the context of the TV show, The Office. Rao uses “Sociopath” in place of “Missionary” in his piece, but for our purposes here the terms are interchangeable.

It’s important to understand that when Rao writes about Sociopaths, he’s not writing narrowly about serial killer wannabes. He’s writing about people who want to know The Truth. Specifically, Sociopaths want unmediated access to the Truth, because they (rightly) suspect other people have a vested interested in obscuring or distorting it for their own ends. The Beginner Sociopath is vaguely aware of Narrative. In pursuit of Truth she begins unmasking reality–ripping away Narrative abstractions.

Over to Rao:

As the journey proceeds, Sociopaths progressively rip away layer after layer of social reality. The Sociopath’s journey can be understood as progressive unmasking of a sequence of increasingly ancient and fearsome gods, each reigning over a harsher social order, governing fewer humans. If morality falls by the wayside when the first layer is ripped away, other reassuring certainties, such as the idea of a benevolent universe, and predictable relationships between efforts and rewards, fall away in deeper layers.

With each new layer decoded, Sociopaths find transient meaning, but not enduring satisfaction.

Much to their surprise, however, they find that in the unsatisfying meanings they uncover, lie the keys to power over others. In seeking to penetrate mediated experiences of reality, they unexpectedly find themselves mediating those very realities for others. They acquire agency in the broadest sense of the word. Losers and the Clueless delegate to them not mere specialist matters like heart surgery or car repair, but control over the meanings of their very lives.

So in seeking to unmask the gods, they find themselves turning into the gods.

When they speak, they find that their words become imbued with divine authority. When they are spoken to, they hear prayerful tones of awe. The Clueless want to be them, Losers want to defer to them.

[…]

Once the Sociopath overcomes reality shock and frames his life condition as one defined by an absence of ultimate parental authority, and the fictitious nature of all social realities, he experiences a great sense of unlimited possibilities and power.

Daddy and Mommy are not here. Anything is possible, and I can get away with anything. I can make up any sort of bullshit and my younger siblings will buy it.

The sense of freedom is one I like to describe as free as in speech, and as in lunch.

Free as in speech describes the Sociopath’s complete creative freedom in scripting social realities for others. Cherished human values are merely his crayon box.

Free as in lunch describes the Sociopath’s complete freedom from accountability, in his exercise of the agency ceded to him by the Losers and Clueless, via their belief in the reality of social orders.

Non-Sociopaths dimly recognize the nature of the free Sociopath world through their own categories: “moral hazard” and “principal-agent problem.” They vaguely sense that the realities being presented to them are bullshit: things said by people who are not lying so much as indifferent to whether or not they are telling the truth. Sociopath freedom of speech is the freedom to bullshit: they are bullshit artists in the truest sense of the phrase.

What non-Sociopaths don’t recognize is that these aren’t just strange and unusual environmental conditions that can be found in small pockets at the tops of pyramids of power, such as Lance Armstrong’s racing team, within a social order that otherwise makes some sort of sense.

It is the default condition of the universe. The universe is a morally hazardous place. The small pockets of unusual environmental conditions are in fact the fictional realities non-Sociopaths inhabit. This figure-ground inversion of non-Sociopath world-views gives us the default perspective of the Sociopath.

Non-Sociopaths, as Jack Nicholson correctly argued, really cannot handle the truth. The truth of an absent god. The truth of social realities as canvases for fiction for those who choose to create them. The truth of values as crayons in the pockets of unsupervised Sociopaths. The truth of the non-centrality of humans in the larger scheme of things.

When these truths are recognized, internalized and turned into default ways of seeing the world, creative-destruction becomes merely the act of living free, not a divinely ordained imperative or a primal urge. Creative destruction is not a script, but the absence of scripts. The freedom of Sociopaths is the same as the freedom of non-human animals. Those who view it as base merely provide yet another opportunity for Sociopaths to create non-base fictions for them to inhabit.

Regardless of how I qualify it in advance, the word Sociopath carries with it decidedly negative connotations. But again, Sociopaths as described here are not inherently evil. Rao only tangentially touches on the difference between Good Sociopaths and Evil Sociopaths. Here it is: Good Sociopaths choose to adhere to some kind of moral code. Evil Sociopaths choose to live in a state of amorality.*

I’ll expand on this slightly.

The Evil Sociopath embraces nihilism as a license to treat others as playthings. Most often Evil Sociopaths do this through legal means, for example under the cover of business and financial dealings. Others do it through criminal activity, or by playing manipulative games within their personal relationships. And yes, a very small minority of Evil Sociopaths go the serial killer route.

The Good Sociopath, on the other hand, rejects nihilism as a license to treat others as playthings. Critically, this is not because there is some fundamental, verifiable Truth out there affirming an underlying moral order. Instead it’s because, for whatever reason, Good Sociopaths find the thought of embracing nihilism repulsive. The Good Sociopath chooses to believe other people are worthy of some level of dignity.

I have been annoyingly consistent in highlighting the word choose here just to emphasize that we’re dealing with subjective reality. Social systems are reflexive. Facts and small-t truth do exist, but to Sociopaths they’re negotiable.

In the immortal words of Don Draper: “if you don’t like what’s being said, change the conversation.”

And the Sociopath/Missionary is free to do so.

Free as in speech.

Free as in lunch.

* For another pop culture reference that may make this more concrete, the first season of HBO’s True Detective is pretty explicitly about Rust Cohle’s Sociopath journey, and how he and various and sundry other Sociopaths cope with “reality shock.”

The Confidence Meter

If you are anywhere near as strange a person as me, you spend a lot of time thinking. And not only thinking, but thinking about thinking (whether any good ideas actually come out of this process is a discussion for another time). Over the years I’ve become more and more interested in epistemology. Is there a such thing as Truth with a capital T? If so, how would we know if we found it? How can we better manage the Bayesian updating of our priors?

Personally, as far as epistemology is concerned, I come down on the side of fallibism. Whether fallibism is, or should be, applicable to moral questions lies beyond the scope of what I write about here. But when it comes to our beliefs about economics, geopolitics, and investing, I think fallibism is an eminently sensible position.

Now, it’s important to distinguish between fallibism and nihilism.

Nihilism is extreme skepticism in the existence of Truth.

Fallibism is extreme skepticism in the provability of Truth and in the methods we use to arrive at Truth. (See also: The Problem of Induction; The Trouble With Truth)

For a fallibist, acquiring knowledge is a relentless, grinding process of formulating conjectures, challenging them, adjusting them, discarding them, and so on. It never ends. By definition, it can’t end. So if you’re bought-in on fallibism, you need to seek out people and ideas to challenge your priors.

This is not fun. In fact, we as humans pretty much evolved to do the opposite of this. For most of our history, if you were the oddball in the tribe you risked being exiled from the group to make your way in a harsh and unforgiving world, where you would likely die miserable and alone (albeit rather quickly), without the consolation of having passed along your genetic material.

The Confidence Meter is a little mental trick I use to mitigate my evolved distaste for challenging my priors, as well as my evolved distaste for being wrong. It’s something I think about when I want to judge how tightly to grip an idea (such as an investment idea). It also helps interrupt emotional thought patterns around certain ideas. For fans of Kahneman, I use it to interrupt System 1 and activate System 2.

The Confidence Meter (A Stylized Example)

At 0% confidence, I shouldn’t even be making a conjecture. At 0% confidence, I should just be gathering information, and soaking it all in without an agenda. (Not always easy)

At a toss-up, I could make a conjecture and support it with evidence, but I wouldn’t put anything at risk.

At 75% confidence and greater, a willingness to bet money on the outcome implies a sound grasp of the theory underlying my idea, as well as the empirical evidence. It also implies I have a sound grasp of the arguments and empirical data challenging my position.

Using this framework, how many of your beliefs do you suppose merit a >=75% confidence level?

For me, it’s a very small number. To the extent I’m >=75% confident of anything I believe, it’s elementary, almost tautological stuff, like how you make money investing.

The empirical data around the impact of the minimum wage on unemployment? Meh.

The relationship between marginal tax rates and economic growth? Meh.

That doesn’t mean I don’t have beliefs about these things. I’m just leaving an allowance for additional dimensions of nuance and complexity. Particularly when we’re inclined to look at relationships in linear, univariate terms for political reasons. The world is a more complex place than that admittedly powerful little regression model, Y = a + B(x) + e, would lead us to believe.

The Confidence Meter helps me keep that in perspective.

Mental Model: Market Regimes

Markets and economies go through cycles. We’re used to hearing about bull markets and bear markets. We’re used to hearing about economic booms and recessions. But we don’t talk quite as much about market regimes.

A regime is a particular iteration of a particular phase (or phases) of a market cycle. Understanding regimes is important because markets are adaptive systems. Investors respond dynamically to changes in the economic environment, since changes in the economic environment influence their preferences for different cash flow profiles. As I wrote here, these changing preferences are key drivers for asset prices.

What characteristics define a regime? Things like:

Economic growth
Inflation
Interest rates (cost of capital)
Credit expansion/contraction
Market volatility

Every market regime is a bit different, but regimes tend to influence investor behavior in relatively predictable ways (partly the intuition behind the old saw: “history doesn’t repeat, but it rhymes”). In a deflationary regime, investors sell stocks and buy long-dated Treasury bonds. In an an inflationary regime, investors sell long-dated bonds, while bidding up real assets. In a growth regime, investors will bid up stocks at the expense of long-dated bonds.

Of course, this is a massive oversimplification. Identifying and profiting from market regimes is no easy feat. That’s the goal of the top-down global macro investor, and it’s an extraordinarily complex and difficult task.

So what do us mere mortals take away from this?

We want to ensure our financial plans and investment portfolios remain robust to different market regimes. This doesn’t mean we have to become market timers or macro forecasters. It means we should be thoughtful about the bets we’re embedding in our portfolios.

Unintended Bets

Today, the consensus view is that we’re in a “lower for longer” regime. Low growth. Low inflation. Low interest rates. There are big secular drivers behind this. In developed countries, older populations need to save a lot of money to fund future liabilities. Lots of investment capital in need of a home pushes down the cost of capital. Technological advances have kept a lid on inflation in many areas of daily life.

If the regime is “lower for longer,” what you want to bet on is duration.

We can define duration in different ways. Usually we’re talking bond math. In this context, duration is the sensitivity of a bond’s price to changes in interest rates. The longer a bond’s future cash flows extend out into the future, the higher its duration. The higher the duration, the more sensitive the bond will be to changes in interest rates. The archetypical high duration asset is the zero coupon bond.

If the market regime is “lower for longer,” you have an incentive to bet on large cash flows further out into the future. Low rates and low growth mean the opportunity cost for making these bets is also low.

Duration isn’t just a bond thing. Every asset with cash flows also has duration. It’s just harder to quantify for equities and real estate because of the other variables influencing their cash flow profiles.

Your venture capital investments? They’re a duration bet.

Your small cap biotechs? They’re a duration bet.

Your cash burning large cap growth equities? They’re a duration bet.

All these things are attractive in a “lower for longer” world because they offer Growth! But they’re also sensitive to the cost of capital. In a world of cheap capital, it’s easy to convince investors to subsidize losses for the sake of Growth! If and when the regime changes, that may no longer be the case.

As much as we hate to admit it, our portfolios are products of our environment. It’s what people are talking about when they say “don’t fight the market” and “don’t fight the Fed.” They might as well be saying, “don’t fight the market regime.”

As I’ve written many times before, I’m not a fan of “all-in,” “all-out” calls. That doesn’t just go for market timing. It goes for all the unintended bets that seep into our portfolios over time.

Especially those driven by market regimes.

Wunderwaffen

One theme I harp on relentlessly is that there’s no such thing as a magical investment strategy. By “magical strategy” I mean some asset class or system that’s inherently superior to all others. Hedge funds were once sold this way, and we’ve spent the last 10 years or so watching the ridiculous mythology built up around hedge funds die a slow and miserable death.

The unpleasant truth is that all investment strategies involve tradeoffs. In this way, investment strategies are a bit like weapons systems.

Tank design, for example, must balance three fundamental factors:

Firepower
Protection
Mobility

This is a Tiger tank:

Bundesarchiv_Bild_101I-299-1805-16,_Nordfrankreich,_Panzer_VI_(Tiger_I).2 — Source: Bundesarchiv via Wikipedia

You might recognize it from any number of WWII movies and video games. The Tiger is often presented as a kind of superweapon (German: Wunderwaffe)–an awe inspiring feat of German engineering. In many respects, the Tiger was indeed a fearsome weapons system. Its heavy frontal armor rendered it nearly invulnerable to threats approaching head-on. Its gun could knock out an American M4 at distance of over a mile, and a Soviet T-34 at a little under a mile.

The Tiger had its weaknesses, however, and they were almost laughably mundane. It was over-engineered, expensive to produce and difficult to recover when damaged. Early models in particular struggled mightily with reliability. The Tiger was also a gas guzzler–problematic for a German panzer corps chronically short on fuel.

Viewed holistically, the Tiger was hardly a magical weapon. The balance of its strengths and weaknesses favored localized, defensive operations. Not the worst thing in the world for an army largely on the defensive when the Tiger arrived on the battlefield. But it was hardly going to alter the strategic calculus for Germany. In fact, there’s an argument to be made that German industry should have abandoned Tiger production to concentrate on churning out Panzer IV tanks and StuG III assault guns. (Thankfully, for all our sakes, it did not)

Likewise with investment strategies, the tradeoffs between certain fundamental factors must be weighed in determining which strategies to pursue:

Alpha Generation
Liquidity
Capacity

Alpha generation is typically inversely related to liquidity and capacity. The more liquid and higher capacity a strategy, the less likely it is to consistently deliver significant alpha. Smaller, less liquid strategies may be able to generate more alpha, but can’t support large asset bases. Investment allocations, like military doctrine, should be designed to suit the resources and capabilities at hand.

If I’m allocating capital, one of the first things I should do is evaluate my strategy in the context of these three factors.

First, do I even need to pursue alpha?

If so, am I willing and able to accept the liquidity constraints that may be necessary to generate that alpha?

If so, does my strategy for capturing alpha have enough capacity for an allocation to meaningfully impact my overall portfolio?

In many cases, the answer to all three of those questions should be a resounding “no.”

And that’s okay! Not everyone should be concerned with capturing alpha. For many of us, simply harvesting beta(s) through liquid, high-capacity strategies should get the job done over time. Identifying strategies and investment organizations capable of sustainable alpha generation ex ante is extremely difficult. And even if we can correctly identify those strategies and investment organizations, we must have enough faith to stick with them through the inevitable rough patches. These are not trivial challenges.

But even more importantly, in a diversified portfolio it’s unlikely you’ll deploy a single strategy so powerful and reliable, and in such size, that it completely alters your strategic calculus. In general, we ought to spend more time reflecting on the strategic tradeoffs facing our portfolios, and less time scouring the earth for Wunderwaffen.

Mad Scientist Futurists

“Any sufficiently advanced technology is indistinguishable from magic.” –Arthur C. Clarke.

In my line of work, I sit in a lot of meetings. I listen to a lot of conference calls. I attend a lot of conferences. Many of these are thinly disguised sales presentations. Sure, we don’t call them sales presentations. We call them “continuing education” or “maintenance due diligence” or “onsite manager visits.”

You sit through enough of these meetings and you begin to recognize different archetypes.

There’s The Brilliant Introvert.

The Streetwise Deal Guy.

The Mad Scientist Futurist.

That last fellow is most often encountered in group settings. Particularly conferences. I’d venture to say the industry conference is the Mad Scientist Futurist’s natural habitat.

Now, the guy doesn’t necessarily predict the future. He might merely describe a possible future. These days, for example, the future seems to involve a lot of e-sports and e-commerce and machine learning. A few years ago it was drones and 3D printers as far as the eye could see.

The Mad Scientist Futurist spends a lot of time on his description of the future. And it’s not just him up there talking, mind you. He’s got sales collateral. He’s got data and charts and renderings. He does Science!, remember? After about fifty slides of this, it all takes on a certain aura of inevitability.

And then, finally, once he’s painted a sufficiently fantastical image of the future, the Mad Scientist Futurist tells you what the future means.

He tells you what it means for society. He tells you what it means for the economy. He tells you what it means for your portfolio.

The Mad Scientist Futurist is a modern incarnation of the wizard! meme. We seem hardwired to respond to wizards as symbols, both in stories and in life. Merlin is a wizard. Faust is a wizard. Elon Musk and Satoshi Nakamoto are wizards.

We have an innate weakness for wizards and Mad Scientist Futurists precisely because we’re hungry for meaning and Narrative. When we see ~~magic~~ advanced technology in action, our tiny minds can scarcely comprehend the implications. We want someone to walk us through them. We need someone to walk us through them.

This isn’t necessarily a bad thing.

But it can be dangerous.

Because memes and archetypes are so powerful, they’re easily weaponized to sell us stuff. The Mad Scientist Futurist is typically deployed to sell what the style box classifies as “growth” investments–expensive tech stocks and venture capital and such.

Now, there’s nothing inherently wrong with owning expensive tech stocks and venture capital and the like. But you should own it because you’ve made a conscious decision about the role it plays in your portfolio–not because a Mad Scientist Futurist cast a Buy! spell on you.

Mental Model: Value vs. Momentum

We’ve discussed at length how asset prices are driven by changes in investor preferences for different cash flow profiles. I’ve explored this both here and here. In this post, I suggest those preferences are grounded in two psychological profiles: mean reversion (value) and trend (momentum).

The psychology of mean reversion assumes all things revert toward long-run averages over time. Today’s winners will win a little less. Today’s losers will win a little more.

The psychology of trend assumes winners keep on winning, and losers keep on losing.

The more time I spend with investors and savers of varying sophistication levels, the more I believe people are hardwired for one or the other.

Personally, I’m hardwired for mean reversion. It’s extremely difficult for me to extrapolate strong growth, earnings, or profitability into the future. It’s painful–almost physically painful–for me to own popular stuff that’s consistently making new highs. If I happen to be winning in the markets, it invariably feels too good to be true.

A trend guy is just the opposite. Why own stuff that sucks? he asks. Stick with what’s working. It’ll probably get better over time. If anything, you should be shorting the losers.

A popular misconception about value and momentum guys is that value guys buy “cheap” stuff and momentum guys buy “expensive” stuff. I used to think this way. And I was wrong. For a long time I fixated on the headline valuation multiples of the stuff each personality owned, totally ignorant of what was going on under the hood.

The value guy says:

This security is pricing such-and-such a set of expectations, which reflect the naïve extrapolation of present conditions. This, too, shall pass. When expectations re-rate to properly reflect the characteristics of the underlying cash flow stream, I will exit at a profit.

The momentum guy says:

This security is pricing such-and-such a set of expectations, but those expectations aren’t high/low enough. When expectations re-rate to properly reflect the characteristics of the underlying cash flow stream, I will exit at a profit.

Of course, there’s another guy relevant to this discussion. That’s quant guy. Quant guy steps back and thinks, “gee, maybe all these mean reversion guys’ and trend guys’ psychological dispositions impact security prices in relatively predictable ways.” Quant guy decomposes the mechanics of value and momentum and builds systems for trading them. Quant guy catches a lot of flak at times, but I’ll say this for him: he tends to have a pretty clear-eyed view of how and why a given strategy works.

In closing, I want to suggest all fundamentally-oriented investment strategies, whether systematic or discretionary, are rooted in the psychology of value and momentum. Both have been shown to work over long periods of time. However, they don’t always (often?) work at the same time. Arguably, this inconsistency is directly responsible for their persistence.

Put another way: value and momentum tend to operate in regimes.

And regimes deserve a post all their own.