The Skinner Box

Skinner_box_scheme_01
Source: Wikpedia

A Skinner box is a device used to study animal behavior. Its more formal name is “operant conditioning chamber.” It was originally devised by the behavioral psychologist B.F. Skinner. Skinner used his box to study how animals respond to positive or negative stimuli. For example, a rat can be conditioned to push a lever for a bit of food. A dog can be conditioned to salivate whenever a bell rings.

Lest you be inclined to dismiss operant conditioning as silly games played with animals, it’s worth considering that slot machines, video games and social media all make use of operant conditioning to shape our behavior.

The financial markets, too, are a kind of Skinner box.

Do you suppose we believe what we believe about investing because there are immutable laws, similar to physical laws, that govern the price action in markets?

LOL.

We believe what we believe about investing because we’ve been conditioned to believe it. Much of what we think we “know” about investing is simply rationalized, conditioned behavior (the endless and pointless debate over “lump sum versus dollar cost averaging” is a perfect example–the “answer” is entirely path dependent). We investors aren’t so different from Skinner’s rats, working their little levers for their food pellets. It’s just that we’re after returns instead of snacks.

Below is what an operant schedule of reinforcement looks like.

Bet on Market Factor -> REWARD (GOOD RETURNS, CLIENTS HIRE YOU)

Bet on Momentum Factor -> SMALL REWARD (MAYBE)

Bet on Value, Size, Quality -> PUNISHMENT (BAD RETURNS, CLIENTS FIRE YOU)

1Q19_Rolling_Factors
Data Source: Ken French’s Data Library
1Q19_Trailing_Factors
Data Source: Ken French’s Data Library

The “lesson” here is very clear:

BETA IS ALL THAT MATTERS

BETA IS ALL THAT WORKS

This is what public market investors are being conditioned to believe. And if flows away from active management (particularly low beta strategies) are any indication, the market Skinner box is doing an admirable job. Demand for investment strategies is all operant conditioning, all the time.

Of course, the markets are more complicated than Skinner’s box. Market price action is both the input and output of investor behavior. It’s more like a Skinner box where the collective actions of the rats influence the operant schedule of reinforcement (this is another way of thinking about the concept of reflexivity).

The idea of markets-as-Skinner-boxes is inextricably linked to the idea of market regimes: patterns of correlations for economic variables such as interest rates, economic growth and inflation. It’s also inextricably linked to the idea of the zeitgeist: “the spirit of the age.” The relationship between these processes doesn’t flow so much as interlock. Each process acts on the others.

Regime_Graphic

This visualization isn’t ideal. It implies the interactions are mechanical in nature, and that the result is a straightforward, predictable system. It’s not. In reality it’s much more an interaction of planetary bodies and gravitational fields than clockwork mechanisms of wheels and gears. My friends Ben and Rusty describe this as the three body problem. But imperfect as the above visual may be, it gives you a rough idea of how all this interrelates.

Reality Check

This is a short-and-sweet post meant to get some thoughts down and possibly provide a (small) public service. In my line of work I’m involved in a bit of direct private equity investing. The typical acquisition target is a Main Street USA business with EBITDA somewhere between $500,000 and $2,000,000. These are profitable businesses but slow growers. We’re talking mid-single digit revenue growth here.

These businesses are worth something like 3x to 5x EBITDA (subject to negotiation, of course). It’s rare that the seller is financially sophisticated. The sale of the business is probably the only such transaction he or she will complete in a lifetime. So setting realistic expectations around pricing is one of the most important things to cover early in the process. If someone thinks he’s going to get a 10x multiple on one of these things it’s best to walk away early rather than waste everyone’s time and energy.

There’s a common argument unsophisticated sellers trot out to make the case for a higher valuation. It’s this:

What about my IP and intangible assets? Surely they’re worth something. You should be assigning more value to those things!

No purchaser takes this argument seriously. The fact of the matter is that value has been assigned to the IP and intangible assets. It’s in the earning power of the business.

Put another way, when you buy an operating business you don’t buy the tangible assets (property and equipment) separately from everything else. Same with intangibles. The costs and benefits associated with both tangible and intangible assets are loaded into the cash flow profile of the business. You don’t double-count them.

A Koan

I’ve been reading Zen stories lately, and even trying my hand at amateur koan introspection. Ben’s latest Epsilon Theory note opens with a story that makes for an interesting koan. Here’s the story:

Back in the day, the long/short hedge fund I co-managed was part of a larger long-only asset manager. Their biggest strategy was US mid-cap value, and it was well staffed with a bevy of really sharp analysts and PMs. But the firm also had a $4 billion US large-cap strategy that was managed by all of two people – the firm’s co-founder as PM, plus a single analyst position that was something of a revolving door … people would come and go all the time in that seat.

The solo analyst’s job, as far as I could tell, was basically to go to investor conferences and to construct massive spreadsheets for calculating discounted cash flow models for, like, Google. And sure, Google would be in the portfolio, because Google MUST be in a large-cap portfolio, but it had nothing to do with the literally hundreds of hours that were embedded in this sixty page spreadsheet. I mean … if the firm’s co-founder/PM spoke with the analyst more than once per week about anything, it was an unusual week, and there’s zero chance that he ever went through this or any other spreadsheet. Zero.

Now to be clear, I think the firm’s co-founder was a brilliant investor. This guy GOT IT … both in terms of the performance of portfolio management and the business of asset management. But here he was, managing a $4 billion portfolio with one ignored analyst, and it was working just fine.

And here’s my koan-version:

A portfolio manager was doing a fine job managing a $4 billion stock portfolio.

A single analyst worked under him, coding up elaborate fundamental models of portfolio companies. The portfolio manager never spoke to the analyst, nor reviewed the models he created.

Over the years, many analysts came and went. It was always the same story with them. But the portfolio continued on in the same way. It was working just fine.

The Eye of the Beholder

MasonicEyeOfProvidence

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.

Having Bought The Dip…

The Fed’s 4Q18 Z1 data is out so I am able to update this little model of prospective 10-year returns for the S&P 500. If we could run this again today I suspect it’d be forecasting about 6-7% for the next 10 years, given how we’ve rallied in 1Q19. Not spectacular but not awful, either. Clearly, in the aggregate we bought the dip.

4Q18_SP_ER
Data Sources: Federal Reserve Z1 Release & Demonetized Calculations

Below is the latest expected returns bar chart from RAFI. Definitely a less optimistic picture for US large cap equities, but I believe this is a (small) improvement over the last time I checked it. The “obvious” relative value play is of course ex-US equity and emerging market equity in particular. I put “obvious” in scare quotes here because there are real risks to tilting a portfolio this way, as I’ll discuss a bit more below.

20190308_RAFI
Source: Research Affiliates

This is merely a brief analytical exercise for perspective. Like all models, these ones have weaknesses. The most significant, in my view, is they’re not “macro aware.” For example, the S&P 500 model above would not have given you any warning of the global financial crisis. Today, as far as the differences in relative valuation between US and ex-US equities are concerned, we live in a time where there is a strong argument to be made that globalization is unraveling. And if globalization truly unravels, the intuition underlying global equity investing unravels along with it.

The two risks I worry most about these days?

Geopolitical fragmentation. Taken to a certain extreme, this would break the idea of a globally diversified equity portfolio.

A major spike in inflation. This would break the traditional 60/40 portfolio, at least in real terms.

These risks don’t just represent asset price volatility. They represent regime changes. They represent changes in the relationships between financial assets–changes in financial gravity. My friend Ben Hunt has written what I think is the best piece about what an inflationary regime change means for investors. The short version is that it’s the death of the long bond as an effective diversifier.

Geopolitical risk is trickier. I’m extremely skeptical anyone can effectively handicap geopolitical risk. It’s not something you predict. It’s something you observe. You deal with it when it manifests in the real world, as it happens. Of course, in theory you can hedge this kind of risk. The folks who sell this protection aren’t usually in the habit of giving it away at firesale prices, though I guess it never hurts to check around. Every once in a while you will find something stupid cheap like VIX calls circa 2017 and early 2018.

There is another factor in play here that I don’t consider so much a “risk” as a “force” that acts on everything else. That is fiscal and monetary policymaking–particularly monetary policymaking. Our friendly neighborhood central bankers have made it overwhelmingly clear they intend to remain supportive of financial markets. This will shape the market regime and therefore the relationships between financial assets. Like geopolitical risk, it’s not something you can effectively handicap. As I’ve written elsewhere:

Fed Watching is the ultimate reflexive sport. If you believe there is some kind of capital-T objective Truth to be found in Fed Watching, I am sorry to be the one to tell you but you are one of the suckers at the table. The Fed knows we all know that everyone knows the information content of Jay Powell’s statements is high. (We call them Fed Days, for god’s sake) The Fed plays the Forward Guidance Game accordingly. Sometimes it uses its “data” and “research.” Sometimes it speaks through one of its other hydra heads. The tools and tactics vary, but they’re all deployed to the same end: to shape the subjective realities of various economic and political actors.

I am critical of the current approach to monetary policymaking both in the United States and abroad. However, I do not think shorting the world or sitting 100% in cash or gold is a particularly good strategy. Two reasons:

  1. If the world ends you are probably not going to have much fun collecting on your bet because it will be the end of the global financial system as we know it. You’re better off investing in guns and ammo and maybe a bunker somewhere to express this view.
  2. You are betting against the combined fiscal and monetary policymaking apparatuses of every country in the world. Kind of like shorting a stock where the CEO has unlimited cash available to buy back stock.

Personally, I’m doing my best to balance cautious optimism with a healthy amount of paranoia.

You Got To Pay To Learn

“A knowledge of cheating methods and the ability to detect them is your only protection against dishonest players in private games. It is for this reason that the most ethical, fastidiously honest card games are those in which the players are top notch gamblers, gambling operators, gambling-house employees and card sharpers. When they play together the game is nearly always honest. It has to be, because they play in an atmosphere of icy distrust, and their extensive knowledge of the methods of cheating makes using their knowledge much too dangerous. They do not cheat because they dare not.

In a money card game patronized by men and women who know little or nothing about cheating techniques, the odds are 2 to 1 that a card cheater is at work.”

John Scarne, Scarne’s New Complete Guide to Gambling (1986)

I came down with some horrible, vaguely flu-like illness recently. Happily, this at least coincided with the arrival of a print copy of Scarne’s New Complete Guide to Gambling. It’s more of a reference text than something you’d read cover to cover. What impressed me most as I leafed through its 800-plus pages was Scarne’s obsession with cheating. In addition to a full sub-section on methods for cheating at card games, nearly every discussion of a game includes a section on common methods for cheating. Rigged games, it seems, are the default state of the universe.

Scarne’s dismissal of carnival wheels is typical of both his logic and wit:

If you want to play carnival wheels for fun, you would be smart to consider that 25% to 50% of the money you wager on each spin is a donation; when you reach the total amount you wish to donate—quit playing.

As you’d expect, in addition to cheating, Scarne was fairly obsessed with edge. Of poker strategy, he writes:

There is one big secret, a Poker policy which, if put to use, will not only make you a winner at your next session but at most of them. It’s a policy that is practiced religiously by the country’s best poker hustlers. It is the only surefire rule that wins the money. It’s a simple rule: Don’t sit in a Poker game with superior players.

There are plenty of ways to apply this rule to investing. It’s well-worn ground in the context of the active/passive debate. I’ve got little to add there. So let’s talk about another application. Let’s talk about deals. Specifically, let’s talk about the “democratization” of deals—how increasingly, private equity and credit strategies are being pitched to wealthy individuals and their financial advisors as important, if not essential, additions to portfolios.

I’m hardly a low complexity, liquid asset teetotaler when it comes to portfolio construction. I happen to believe private market deals offer a rich opportunity set for value-added portfolio management by skilled professionals.

Why? Because we’re talking about a relatively inefficient, illiquid market where the participants are allowed to act on inside information. A real poker game. A wild west poker game, even. I suspect John Scarne would feel right at home at the helm of a PE or VC shop.

And wouldn’t you know it, the dispersion of returns for non-core real estate, private equity and venture capital managers is immense.

JPM_Alt_Manager_Disperion

What is the single most important thing that separates a top quartile manager from a bottom quartile manager?

Deal flow.

To continue with our poker game analogy, in the larger cap areas of public markets you can be reasonably certain the cards have been dealt fairly. Deal flow isn’t an issue there. In private markets, it’s just the opposite. Private markets are about card sharping. Pickup stacking. Riffle stacking. False shuffling. Nullifying the cut. Bottom dealing. There’s a reason certain big firms’ shticks are recruiting a vast army of consultants and partners, many of whom who operate at the nexus of government and business. There’s far too much money at stake here to leave these things to chance.

I have a friend who did a tour as a White House Fellow. Believe me when I tell you the deck is stacked. The big PE shops and consultancies are masters of the riffle stack.

So where does that leave us?

We can either learn to see the angles, or we can decline to play. When it comes to deals, there are plenty of hands not worth playing.

To take a simple example, let’s think about interval funds. These are private equity and credit deals packaged in a mutual fund-like wrapper that can more easily be sold to mass affluent clientele. The pitch is that you, or your financial advisor, can access the private equity “asset class” with more favorable liquidity terms, 1099 tax reporting, and so on. “Private markets for the rest of us,” so to speak.

What’s not to like?

I’ve had the opportunity to discuss these with a couple investment banker types. I always ask the same question: “How can we know the sponsors aren’t just dumping all their worst deals into these retail vehicles as an excuse to charge fees on the assets?”

The answer always comes back: “You can’t.”

And as anyone who’s ever invested in anything even remotely illiquid well knows, favorable liquidity terms are just, like, someone’s opinion. Read the docs! If stuff ever hits the fan, you’ll be gated and locked up like everyone else. No one cares about liquidity when times are good. Everyone wants liquidity when times are bad. The more desperately you want out, the more likely you are to find yourself trapped. This is a timeless axiom of risk management.

Oh, and there’s always the matter of performance evaluation. Deals are sold on the basis of IRR, but “you can’t eat IRR” (it doesn’t measure cash-on-cash returns). So remember to compare IRR to MOIC, and on top of that to look at everything in the context of your original capital commitment, ’cause there’s an opportunity cost to committed capital. You can go on and on with this stuff. We haven’t even gotten to trends for deal multiples, or the dispersion of those multiples across across market segments, or the leverage and coverage levels for those deals, or what any of that might mean for prospective returns…

…so, yeah, there are lots of angles in private markets.

How do you learn to spot them?

The same way you learn to gamble. By playing. By getting fleeced. By losing money. Scarne again:

After twelve hours of gambling, Fat the Butch found himself a $49,000 loser, and he quit because he finally realized something must be wrong with his logic. He was, later, part owner of the Casino de Capri in Havana, and when I told him it would need 24.6 rolls to make the double-six bet an even-up proposition, and that he had taken 20.45% the worst of it on every one of those bets, he shrugged his massive shoulders and said, “Scarne, in gambling you got to pay to learn, but $49,000 was a lot of dough to pay just to learn that.”

I’m not saying you shouldn’t play this game.

I’m saying if you choose to play, you better play to win, and you better be ready to take some hard knocks along the way. DO NOT DABBLE NAIVELY. Because the folks pitching you deals, and their competitors, are definitely playing to win. Winning is their business. I’m not talking narrowly about generating attractive net returns for investors. I’m talking about fee revenue and carried interest. Fee revenue and carried interest are the metagame here. And there’s far too much money at stake to leave that outcome to chance. Regardless of your net returns as an investor.

Since this is a Scarne-inspired note, I’ll give him the last word:

When you play cards, give the game all you’ve got, or get out; not only is that the one way on earth to win at cards, it’s the only way you and the rest of the players can get any fun out of what ought to be fun. You can’t play a good hand well if your mind’s on that redhead down the street or the horses or your boss’s ulcers or your wife’s operation. When you don’t remember the last upcard your opponent picked and you throw him the like-ranked card which gives him Gin, it’s time to push back your chair and say, “Boys, I think I have another engagement.”

Edge Over Odds

Kelly Criterion

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:

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