Trading Is A Team Sport

In Liar’s Poker, author Michael Lewis recounts his days of being a junior salesperson on the Salomon trading desk in the 1980s. He was impressed by a senior trader, Alexander, who took Lewis under his wing.

The second pattern to Alexander’s thought was that in the event of a major dislocation, such as a stock market crash, a natural disaster, the breakdown of OPEC’s production agreements, he would look away from the initial focus of investor interest and seek secondary and tertiary effects.

Remember Chernobyl? When news broke that the Soviet nuclear reactor had exploded, Alexander called. Only minutes before, confirmation of the disaster had blipped across our Quotron machines, yet Alexander had already bought the equivalent of two supertankers of crude oil. The focus of investor attention was on the New York Stock Exchange, he said. In particular it was on any company involved in nuclear power. The stocks of those companies were plummeting. Never mind that, he said. He had just purchased, on behalf of his clients, oil futures. Instantly in his mind less supply of nuclear power equaled more demand for oil, and he was right. His investors made a large killing. Mine made a small killing. Minutes after I had persuaded a few clients to buy some oil, Alexander called back.

“Buy potatoes,” he said. “Gotta hop.” Then he hung up. Of course. A cloud of fallout would threaten European food and water supplies, including the potato crop, placing a premium on uncon taminated American substitutes. Perhaps a few folks other than potato farmers think of the price of potatoes in America minutes after the explosion of a nuclear reactor in Russian, but I have never met them.

But Chernobyl and oil are a comparatively straightforward example. There was a game we played called What if? All sorts of complications can be introduced into What if? Imagine, for example, you are an institutional investor managing several billion dollars. What if there is a massive earthquake in Tokyo? Tokyo is reduced to rubble. Investors in Japan panic. They are selling yen and trying to get their money out of the Japanese stock market. What do you do?

Well, along the lines of pattern number one, what Alexander would do is put money into Japan on the assumption that since everyone was trying to get out, there must be some bargains. He would buy precisely those securities in Japan that appeared the least desirable to others. First, the stocks of Japanese insurance companies. The world would probably assume that ordinary insurance companies had a great deal of exposure…

I read this as a college student, not much younger than Lewis himself when he lived this story. Fancying myself more clever than I actually am, I put myself in Alexander’s shoes. I did well in school without working too hard, so the allure of making money by being quick and clever without working too hard seemed like an obvious next step for a corner-cutter like me. With the benefit of experience, when I think about Liar’s Poker and this passage I’m more struck but what I missed:

1. Alexander was a discretionary trader whose mind compiled logic quickly in an era when he didn’t have to compete with machines that do that particular task faster than a human. No shade to him of course. Ironically, the book talks far more about the math whizzes and PhD’s that Solomon was a pioneer in recruiting to Wall Street to price a brand-new asset class — mortgage-backed securities. Since borrowers have the option to pre-pay their mortgages when rates fall, mortgages and their derivatives required deeper analysis then putting your finger in the air. Smart was not enough. You had to have a demonstrated ability to do quantitative research. I was enamored by Alexander who represented the past, instead of noticing that the nerds were the future.
2. Despite referencing Alexander’s tutelage, Lewis made it seem that Alexander’s guidance wasn’t exactly expected. Alexander just took a liking to Lewis. The mentorship was benevolence. There was no sense that developing juniors was a priority. These were desks of lone-wolf mercenaries, not long-term-oriented businesses focused on succession. The defining trading/investing film of the recent generation was another Lewis story — The Big Short. In his dramatization of the big winners who bet against the “real-estate always goes up” crowd, the most memorable was Michael Burry. Burry is portrayed as an eccentric, autistic contrarian who would have scored 100 on the disagreeable scale of the Big Five Personality test. A quintessential lone wolf, whose sole outlet was a drum kit next to his trading station.

The first point is mostly appreciated by now in the trading community. The top prop shops aren’t offering $400,000 pay packages to recent grads for their well-roundedness. They are looking for math/coding wizards with substantial academic or side-projects on their resumes. For all the talk of being a fox in a world of hedgehogs, the biggest financial head-starts are offered to the pointiest candidates. I would have no chance of getting hired in such a world out of college today. (My timing was lucky I guess but it’s worth remembering how pissed I was early in my career that there were plenty of rich people who barely knew how to use a mouse. Time, as they say, is a flat circle)

The second point about lone-wolfs is worthy of a deeper discussion. The trope of folks like Druckenmiller and Soros ripping yards of futures based on their spidey sense (or aching back) is not useful. They are outlier geniuses with pattern-matching skills that underpin the same fluid intuition you find in the world’s best athletes. The game slows down so they can find the open man a millisecond ahead of the defender.  For everyone else, successful trading is a team sport where the whole is greater than its parts.

The Importance of a Team In Trading

The Value Of Multiple Perspectives

Like a portfolio of uncorrelated bets, a combination of complementary team members can yield results that don’t resemble any one of the individuals. This isn’t always good. If you’re running a relay you still want everyone to be similar in a particular way — namely, fast.

When it comes to cognition and decision-making, having variant perceptions, relationships and skills are highly desirable if all the members can be similar in one way — a shared goal. In the Good Judgment Project, Professor Phil Tetlock and his fellow researchers were looking for what conditions and qualities lead to better forecasts, an activity of obvious importance to investors and traders. 

They discovered that teams performed better than their best individuals.

The results were clear-cut each year. Teams of ordinary forecasters beat the wisdom of the crowd by about 10%. Prediction markets beat ordinary teams by about 20%. And superteams beat prediction markets by 15% to 30%.

Looking behind the numbers, Tetlock identifies a few themes:

• Emergence
  
  Teams are more than the sum of their parts. This cuts both ways…even actively open-minded individuals could surrender to “groupthink”.
  
• Diversity trumps ability

This provocative claim highlights how the aggregation of different perspectives can improve judgment. The key to diversity was, unsurprisingly, cognitive diversity.

The revealing result: When they constructed the superteams they optimized for ability and those teams happened to be highly diverse because the superforecasters themselves were highly diverse. They did not optimize for diversity first, but it turned out the most diverse teams were the most effective.

• The asymmetry of the extremizing algorithm
  
  The “extremizing algorithm” is a technique where you boost a 70% prediction closer to the extreme, perhaps bumping it to 85%. It’s a technique that is employed when the forecasters have diverse perspectives because it leads to better-calibrated forecasts.You do the opposite (push the forecast probability closer to 50%) to combat “groupthink” if the team is comprised of people who think the same or possess similar knowledge. The use of the extremizing algo allowed teams of regular forecasters to actually perform better than some superteams![My own observation: this is the same logic by which correlated observations “shrink” the sample size, an idea familiar to data analysts.]

Tetlock’s discoveries demonstrate the superiority of teams. The group’s diverse thinkers simulated a consensus-finding mechanism akin to how markets generate “best guesses”. In Superforecasting, Tetlock explains:

Bits of useful and useless information are distributed throughout a crowd. The useful information all points to a reasonably accurate consensus while the useless information sometimes overshoots and sometime undershoots but critically…cancels out.¹

How A Team Reduces Bias In Practice

Corporations are cooperative environments with shared values, operating in adversarial ecosystems. They want to make better decisions. They should try to hire diverse candidates that, like the relay are all “fast” in whatever that means in context. If an employee’s responsibility and impact on the business is in proportion to their decision-making ability, then we’d expect an internal “wisdom of crowds” mechanism to select for merit.

This is where anyone that’s actually worked in a large organization starts laughing. Politics, nepotism, and countless other biases are sand in the gears of meritocracy. This isn’t news. But let’s accept that a full-figured version of meritocracy, where inclusion is understood to be a long-term advantage, is the goal. We can invent a story about a prop firm trading its own money as a place that has strong incentives to pursue this full-figured meritocracy.

Trading firms have a special reverence for the wisdom of markets. Their internal obsession with separating skill from luck gives them a fighting chance of allocating the best decision-makers to the highest-leverage seats in the org chart. Trading firms would like to hire for diversity to incant its potential to spark internal crowd wisdom.

But there’s a problem.

Depending on where they set the minimum threshold for “fast” in their relay, they will struggle to hire, cost-effectively, enough candidates that are both sufficiently “pointy” and diverse (this is a chicken-and-egg problem circumscribed by today’s culture war…again beyond this post).

Recognizing the perils of bias, how can a firm inoculate itself?

The short answer is: “with great difficulty”.

In Notes From Todd Simkin On The Knowledge Project, Shane Parrish points out a grating paradox in cognitive science — knowing our biases doesn’t seem to help us overcome them.

SIG’s Todd Simkin concurs:

It is definitely true that it is sort of descriptive of the past. A lot of these heuristics and biases are things that we can see when we after we’ve already identified that a mistake has been made. And we say, Okay, well, why was the mistake made? Say, oh, because I was anchored, or because of the way the question was framed, or whatever it might be, we have a really hard time seeing it in ourselves.

But we know the cure for this. I wrote:

This is a topic the brilliant Ced Chin has studied in depth. Ced told me that the literature suggests the only way cognitive bias inoculation works is via group reinforcement. I told him that was exactly the cultural DNA when I was at SIG which makes me believe there is a lot of value in being aware of bias. Anytime you replayed your decision process, it was a cultural norm to point out where in the process you were prone to bias.

Todd shares SIG’s prescription to Ced’s diagnosis:

We have a really easy time seeing when someone else is making that type of stupid mistake. A big part of our approach to education is to teach people to talk through their decisions, and to end to talk about why they’re doing what they’re doing with their peers, the other people on their team. If we can do that real-time, that’s great. Often in trading, you don’t have that opportunity, because things are just too immediate. But certainly, anytime things have changed. If you’re doing things differently, it’s a really good time to turn to the traders around you. And the quantitative researchers around you and the assistant traders and your team and say, Hmm, it looks like all the sudden Gamestop is a whole lot more volatile than it was a week ago. Here’s how I’m positioning for this trading. What do you guys think? And have someone say, oh, it seems like you’re really anchored to last week’s volatility. If things have changed that much, you need to move much more quickly than you’re moving right now. So you don’t realize that you’re anchored, that’s the whole nature of being anchored, is that you don’t recognize the outsized importance that the anchor has on your decision, but somebody else who’s a little bit more distant from it can. So if we’re good at encouraging communication, then we’re going to be really good at getting other people to help improve your decision process.

In a sentence, what you need is frequent communication, in a culture that makes it safe to disagree, where the shared value is “truth”.

Todd nails it:

I know that you are fond of pointing out that you are the sum of the five people that you spend the most time with. So if the people that you’re spending the most time with are your co-workers who are thinking about trading the same way you are, then maybe you’re going to combine the same types of errors, it’s certainly better than then trying to act on your own. But even better is if you have a culture that rewards truth-finding, as opposed to rewarding action. If nobody feels personally attacked, because of somebody else pointing out their error, but instead feels like we together have now done more to get closer to, to some truth to the better way to act or the you know, the more accurate, fair value of this asset that we’re trading, then everybody feels like it’s a win. And they will therefore encourage the involvement of the people around them.

Building The Team

At this point, you realize:

1. Trading is highly competitive
2. The competition demands elite decision-making skills
3. You can make much better decisions if you assemble the right kind of team
4. With a team in place, the key is communication

Agustin Lebron’s Advice

One of my favorite voices in the prop game is trader and author of Laws Of Trading is Agustin Lebron.

I’ve extracted his insights from a great thread he appropriately titled Alpha Leak of the Week. It echoes the earlier wisdom:

If you don’t have a small heterogeneous group of people to talk about trading with, stop what you’re doing and build one. Here’s how:

1. Your public twitter presence (reading/following/posting) is not a substitute. Nor is a discord channel with 100 people. You can’t be truly honest and vulnerable to a hundred people, and it’s hard to interactively teach/learn with them.
   
   
2. What you need is tight trusted interaction with a small group of people. 3 is probably too small, 8 is probably too big. h/t @etdebruin for teaching me the value of such a group.
   
   [David Senra on the Founders Podcast mentions Paul Hare’s classic Creativity in Small Groups showed that groups of 4 to 7 are more effective problem solvers because they are more democratic, egalitarian, inclusive, and mutualistic. Bezos echoed this when he said teams should be able to be fed by “2 pizzas”.]
   
3. The group should be heterogeneous. 4 clones of yourself isn’t going to help. Of course, you can go too far in heterogeneity and that’s bad because if you can’t agree on foundations then no communication can happen.
   
   
4. The trick to this is that every member of such a group needs to be highly valuable and complementary to the group bc the group needs to be small. So you have a chicken and egg problem. You’re green, so seasoned traders don’t want you. You need to join a group to grow and not be green anymore. This is where you need to be creative. What can you barter? How can you be useful? This is no different than being a clerk or assistant trader. The clerk needs to be useful to the trader they report to. You need to trade something for that learning. Asking for mentors doesn’t work. Deserve one.
   
   
5. Develop norms for how your group operates. Cadences for conversations, conventions on meta-language (i.e. how to signal levels of confidence in ideas, how to ask usefully questions, etc). The goal is to create a safe collective mind where everyone can learn and develop.
   
   [I’ll add a point to this. SIG’s internal language around expectancy, especially “how many cents of edge are in a trade” had a wider purpose of dispensing with any wishy-washy reasoning. There was room for confidence intervals or error bars around estimates but ultimately people’s guesses needed to be pinned down. At the end of the day, you are not arguing with the market. You are making a concrete bid or offer price. There are no points for sophistry. Just results and the process that led to them.]

Conclusion

If I were trying to be a prop trader from my pajamas, I’d form a Discord channel of sharp, open-minded, truth-seeking, teachable teammates before I even opened a brokerage account.

This group should:

• provide inspiration and ideas
• give you synthetically more reps by sharing experiences so you don’t have to touch every hot stove.
• expand your circle of competence with orthogonal skills/relationships

I’ve written about how Twitter reminds me of the trading pits, but Agustin is right. You have to form a tighter-knit group for it to have accountability and effectiveness. Twitter can help you source willing members.

Remember, when Shane Parrish asks what the most important variables are for being a better decision-maker, he expects Todd might say “probabilistic thinking”.

But Todd did not hesitate with his answer:

Talk more is number one, that beats probabilistic thinking. That beats sort of anything else. Truth-finding is being able to bring in other people in the decision process in a constructive way. So finding good ways to communicate, to improve the input from others. Thinking probabilistically I think is definitely a very, very important piece of trying to diagnose what works by trying to think of where where things fall apart, where people fail. The other place that people fail is falling in love with their decision process and not being open to being wrong. So an openness to feedback to finding disconfirming information to actively seeking out disconfirming information, which is really uncomfortable. But that I think is the other piece that is super important for being a good trader.

Footnotes

1. Many active managers and trend-followers will argue that markets themselves still exhibit “group” behavioral bias and that is their source of edge. There’s an emergence of a group bias that does not need to resemble any of the biases of the individual actors.
   This feels correct (although deserves scrutiny beyond this post). Let’s grant that market prices reflect some net behavioral bias. Perhaps the incentives in a system are sufficiently unbalanced that the market price must inherit the bias. It’s hard to imagine life insurance being fairly priced from an actuarial point of view because there’s a cost to underwrite it. Even in an adversarial environment, the consensus price can be biased as opposed to some platonic vote on the odds of an outcome