The Gamer/Arbitrageur to Generalist Pipeline

Plus! Office Politics, Redux; Vaccine as Perk; Who Owns User Data?; Tech Sees Like a State, Test-and-Trace Edition;The Negotiate-With-Hackers Hack; More...

Welcome back to The Diff. Here are the subscribers-only posts you missed this week:

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In this issue:

The Gamer/Arbitrageur to Generalist Pipeline

Many of the most successful investors in the last generation got  their start in arbitrage before moving on to other things. “Arbitrage”  is a broad term, that ranges from trading the same asset in two  different venues to capture price differences (there used to be good  money in trading the gap between the price of gold in New York, London,  and Hong Kong) to betting on the outcome of a merger that’s been  announced but not consummated.

Warren Buffett was doing complicated three-way arbitrage in the 1950s.[1]  George Soros' first financial job was arbitraging the price gaps between New  York-, London-, and Johannesburg-listed mining stocks. Carl Icahn got  his start making a market in options and warrants, and hedging out the  risk. Before Elliott Management was one of the largest activist  hedge funds, it was a convertible bond arbitrage fund. Robert Rubin’s  arbitrage desk at Goldman in the 70s and 80s produced a steady stream of  hedge fund stars—Richard Perry, Eric Mindich, Eddie Lampert, Dinakar  Singh, and Daniel Och all ran large funds with long winning streaks.  John Paulson focused on merger arbitrage before his record-setting killing  shorting mortgage-backed securities.

Most of these investors did not focus on arbitrage for the rest of  their careers, though. They generalized, and made much of their money  outside arbitrage. Why was arbitrage an unusually good training ground  for other kinds of investing?

It’s a game, and there’s a metagame.

The game is simple: given a current price, an expected future price,  timing, and odds, you can construct a portfolio that maximizes reward  for a given level of risk. When there’s news flow, the first arbitrageur  to react correctly wins. (Jim Cramer tells a story in his memoir about  an oil merger that was rumored to face antitrust action. He was  attending Harvard Law at the time, and casually asked his antitrust  professor about the deal. The professor said it would go through, and Cramer bought.)

At one level, this arbitrage game is a pure example of advantage  gambling. The problem domain is simple. If a company was trading at $30,  it gets an acquisition offer at $40, and the stock trades up to $38,  you can be reasonably confident that you’re risking $8/share to earn  $2.[2]

But how confident are you in that model? Did the stock trade at $30  because that’s what the company was worth to a financial buyer, while a  strategic buyer would pay more? Or did it trade at $30 because everyone  was betting on the deal before it happened, in which case a failed deal  would send the stock lower?

Conversely, once a company has an offer, does this increase the odds  of a bidding war? If nothing else, a deal—or a rumored deal—will cause the company’s shareholder base to consist of fewer long-term holders and more arbitrageurs. Perhaps  the best case study of this is that when Avon Products got a fake takeover offer, the stock rose even once it was clear the offer was fake: arbs had bought the stock, and that made an actual deal more likely. (Avon was eventually taken over, but years later.)

Arbitrageurs have to use every data point to update their model. If a  CEO says something to the media, that’s information; if the same  usually-voluble CEO stops talking to the media, that’s  informative, too. If a company has received an offer, and the stock  trades near the value of the offer, but it starts drifting lower, that’s  something that demands an explanation.

The metagame is less about estimating the probability of a known  event and more about visualizing the entire space of possible events,  and then figuring out the event path from there. That’s a more general  skill, but it’s a skill that’s far easier to apply from a foundation of  converting every data point into an updated view of the odds.

The game is limited, and the metagame is not, but if you’re not great  at the core game, knowledge about the meta is basically useless.

The investors I cited all got their arbitrage done from the 50s  through the 80s. The business has changed: today, the arbitrage business  is well-understood, and there are fewer inefficiencies. As those  efficiencies compress, arbitrageurs' returns on capital become more of a  function of their cost of capital, so independent arbitrage  operations are less viable. That’s made it less of a way for people on  the periphery of finance to work their way in, and more just another  strategy.

Which is not to say the dynamic isn’t still there. It’s still common  for people to succeed in business and investing after spending time on  something that’s a game with a metagame—sometimes, literally gaming.  Shopify’s CEO has praised video games and even hired a former competitive gamer as an intern purely because of his gaming experience. He’s also praised Factorio, saying “It’s the one video game that everyone at Shopify can expense.”[3] Magic: The Gathering is fairly popular among quants, and one of the best players of all time, Jon Finkel, is a managing partner at a hedge fund. Poker, of course, makes a strong showing; it’s a core part of the culture at Susquehanna, a trading firm that happens to own roughly $15bn worth of ByteDance  (this is not money they manage; it’s the partners' money, which makes  them contenders, in dollar terms for the most successful venture  investors of all time). After selling an early startup and before founding Uber, Travis Kalanick was an obsessive Wii Tennis competitor.

In fact, the term “metagame” is mostly used in those fields—real-time  strategy games require a combination of trainable skills, reaction  time, and the ability to identify which playstyles will produce an edge conditional on competing with someone who also has inhuman reaction time.  Collectible card games require continuous inferences about what the  other player can do next, but also how they plan to win. Since each  player is selecting cards from a much larger collection, it’s a field  that rewards adversarial R&D—building a strategy specifically to  beat the currently dominant strategy.

In all of these games—as in arbitrage—success is ultimately bounded  by external rules that are consciously set by third parties. The games  are competitive, and the gap between winners and near-winners is  minimal. There’s such a thing as a persistent skill advantage, but not a  persistent meta advantage, because the metagame is on display every  time someone plays.

Which means all these people have succeeded through a three step process:

  1. Master the game
  2. Master the metagame
  3. Master the meta-metagame of applying the skills necessary for #1 and  #2 to something with less defined rules but unlimited upside.

[1] The actual story is not relevant to the point, but pretty  interesting. A chocolate company Rockwood & Co., had excessive cocoa  inventory. They didn’t want to sell the cocoa and pay a dividend, which  would have been taxable, so they set up a scheme where they’d exchange  cocoa warehouse certificates for stock. Buy a share for $34, exchange it  for warehouse receipts, sell the receipts for $36, repeat. In this  deal, Buffett says the main transaction cost was subway tokens. The deal was a  classic nerd snipe, too: if a company can buy back its stock for less  than the value of its easily-liquidated inventory, the right move is not to do the  arbitrage but to own the underlying stock. And more generally: if you  are offered a deal and feel very smart for accepting, consider how smart  your counterparty might be feeling.

[2] Why doesn’t it trade at $40? The boring answer is the time value  of money, and the interesting answer is a combination of investors'  asymmetric skill and their incentives. If you owned the stock at $30,  you were probably betting on the underlying business, which is a  different skill set from estimating the odds that an acquirer will  successfully complete a deal. And even if you are confident in  that, it’s very hard to tell your boss or your outside investors that  you owned a stock, the company received an offer, you gambled on the  offer going through, and you lost. The gap between where a stock trades  once the offer is made and what the value of that offer is represents a  fee that less specialized stock-pickers pay to arbitrageurs for the  service of handicapping the deal’s odds.

[3] Not that this is an ideal career plan. I have more than one  friend whose career trajectory went vertical when they stopped spending  most of their free time getting very, very good at video games.

A quick note to readers: overwhelming majority of new readers find The Diff  when it’s shared by fans. If you enjoyed this post, please pass it  along to someone who seems to be getting a little tired of the latest  metagame they’ve mastered.

A Word From Our Sponsors

Here’s a dirty secret: part of equity research consists of being one  of the world’s best-paid data-entry professionals. It’s a pain—and a  rite of passage—to build a financial model by painstakingly transcribing  information from 10-Qs, 10-Ks, presentations, and transcripts. Or, at  least, it was: Daloopa uses machine  learning and human validation to automatically parse financial  statements and other disclosures, creating a continuously-updated,  detailed, and accurate model.

If you’ve ever fired up Excel at 8pm and realized you’ll be doing  ctrl-c alt-tab alt-e-es-v until well past midnight, you owe it to  yourself to check this out.


I have a new piece in Medium talking about airlines and loyalty program economics.  Large US airlines' market values right now are below the value of their  loyalty programs, but loyalty economics make it hard to separate the  two.

Office Politics, Redux

Earlier this week, Coinbase’s Brian Armstrong published a blog post arguing that the company should focus on its mission, to the exclusion of other social issues. Shortly thereafter, Coinbase offered severance to anyone who disagreed with the stance. (Covered in The Diff here and here.

This has, naturally, led to a great deal of positive and negative  feedback. On the negative side, many of the critiques argue that since every  company has a political impact, and since many people are deeply  passionate about their politics, it’s both hopeless and exclusionary to  keep that out of the office. This is a valid point of view; while the  original essay explicitly mentions the fact that Coinbase’s aims  interact with the political sphere, it’s always possible to extend that  further.

On the other hand, one critic, former Twitter CEO Dick Costolo, took the opportunity to fantasize about making a snuff film of people who agreed with the memo being executed.  Obviously, this should not be read as a realistic threat of violence—as  the history of social media shows, ideas are easy and execution is  hard—but it highlights how fraught these discussions can get. If  discussions about discussions about politics can prompt public  figures to such violent musings, actual conversations about politics are  bound to be even more intense. Someone can easily agree with the general point Costolo made and also think “I would not want to deal with the fallout if somebody said that on the office Slack.”

One potentially healthy solution is for more companies to be explicitly political in their aims. The CEO letter in Palantir’s S-1  is an effort in this direction. Palantir doesn’t have to worry as much  about political arguments spiraling out of control, because they’ve  tried to select a workforce that a) all agrees that a small subset of  issues are the most important, and b) is on one side of those issues.  (There’s still room for debate, but a debate over “how” is less  distracting than a debate over “what.”) This is more or less what  Coinbase’s critics have read into the Coinbase post, so there’s little  additional cost in actually saying it.

Vaccine as Perk

China’s vaccines are close to formal approval, and are currently  being distributed under emergency-use terms. As a result, they’ve become  a way to trade favors. Per the New Yorker, which quotes a biotech investor in China:

“Some of these friends used to work at Sinopharm, and  they’ve seen people they trust at the company vaccinate themselves,” he  said, explaining that such individuals would have early access to  clinical results. The investor didn’t find this inappropriate, because  participation was voluntary. He pointed out that Gu Fangzhou, the  scientist who developed China’s first live polio vaccine, in 1960, had  administered it to his infant son before mass trials were carried out.  In the United States, Jonas Salk had done the same thing with his own  polio vaccine. At the University of Pittsburgh, Salk’s wife and three  sons were voluntarily injected in 1953, two years before the vaccine was  declared to be safe and effective.

A thirty-four-year-old in Beijing told me that he was offered the  C.N.B.G. vaccine because his company engaged in business with Sinopharm.  This seems to be a uniquely Chinese addition to the Pittsburgh  model—from what I can tell, there’s nothing on the historical record  about Jonas Salk building guanxi dose by dose.

This is the retail form of vaccine diplomacy, which will be an important feature of the world in the coming months.

Who Owns User Data?

Facebook has sued two companies that scrape Facebook’s contents through a browser extension.  The browser extension market usually involves users getting services  and giving away data for free, and this is right at the fuzzy boundary  between the data that belongs to the user and data that’s in Facebook’s  custody. Everything the extensions are tracking is data that Facebook  shows logged-in users, although the extensions generally track other users' information as well.

This is a case where the critique that Facebook owns users' data is  actually quite useful: if the users own the data, Facebook doesn’t have  any grounds to object, but if users don’t, then the extensions are hoovering up information that belongs to Facebook, not to them.

Tech Sees Like a State, Test-and-Trace Edition

Amazon plans to run 50,000 Covid tests a day for its 1.37m front-line employees,  and has identified 19,816 positive cases. Amazon estimates that this is  42% less than the age- and geography-adjusted average for the US. This  testing pace is extremely aggressive relative to the US. Amazon expects to test 3.6% of its employees daily, compared to 0.25% for the US as a  whole. And because Amazon runs a planned economy—with some very careful  planners—it can enact the sort of fine-grained responses that national  governments have struggled to.

Conflicts of Interest

BaFin, Germany’s anti-fraud regulator, has banned employees from trading stocks,  after the discovery that they had been actively trading Wirecard while  it was being investigated for fraud, but before it collapsed. It is, of  course, much more newsworthy that they were doing the trading in the  first place, and might point to an important difference in the  assumptions behind each legal system. US securities regulation tends to  assume that, given the opportunity, traders will exploit every possible  information asymmetry, and that securities laws need to protect against  all the forms of exploitation that cause net harm. In a market where  they’re less rapacious, it might take a while before anyone realizes  that those trades could happen, and it takes a scandal to ban them.

The Negotiate-With-Hackers Hack

Earlier this week, I linked to a story on why cyber-risk insurers tend to pay ransoms rather than try to recover data. The Treasury Department has since warned  that anyone who pays such a ransom needs to ensure that they’re not  violating US sanctions. Since it is not especially likely that a North  Korean hacker syndicate will go through a standard KYC/AML procedure,  this amounts to a ban on paying ransoms for ransomware. Assuming it’s  enforced, that’s very exciting news for everyone in the data-recovery  business, and a nightmare for insurers.

Small Deals and Stagnation

A common theme, in The Diff and elsewhere, is the  observation that public equities markets love any company that can  credibly promise a stream of low-volatility cash flow, even if its  growth is not spectacular. The latest instantiation of this: big banks are increasingly focused on smaller M&A deals.  Large deals have better economics—when they happen. But returns are  lumpy, and may not arrive at all. Smaller deals are less risky on a  per-deal basis, and pursuing a lot of them adds diversification. When  large banks look for smaller deals, they’re admitting that a more  predictable but less profitable advisory business is ultimately what  their shareholders want.