In the past few days, I’ve been getting around to the feedback and follow-ups from last week’s StockSlam sessions. Here’s a reaction and my response worth sharing widely.
Attendee:
Just wanted to shoot you a quick note – loved the game last week, thanks for putting it on!
I had a hard time playing the game because I didn’t have intuition for the odds of the game… I’m way more of a Quant- the only thing I could think of was trying to execute the optimal strategy.
To figure out the optimal strategy I’d run a Monte Carlo simulation – play the game 100,000 times (programmatically using python or something) and see the distribution of outcomes as well as figure out some conditional probabilities (like what are the odds of last place winning given current relative location). Getting a sense of this would help price different bets – not a sure thing all the time, but better odds!
Generally, I ended up playing the game buying out-of-the-money “horses” (i.e. last place)… I figured with the mean reversion built into the game combined with behavioral biases to dump losers would be a winning strategy… and I ended up with a positive PnL so maybe I was into something!
I don’t know how you did that for a career for so long… so stressful and I was wound up all night from it, haha…
My reply:
An anecdotal observation — I’ve noticed that quants and accountants actually get a bit paralyzed sometimes and it highlights the fact that crunching the numbers to perfection isn’t the core skill of trading.
It really is handicapping how wrong you could be and then acting with a margin of safety commensurate with the possible reward. Basically, if you wait to have the best info you’ll be too late. So the constraint is “how do I act optimally subject to being fast?” Everyone is in the same boat. That’s a key point. The game would be different if everyone had infinite time to crunch the numbers. Trading is playing the game at hand — and that has a speed component. This is inescapable. It’s also true in reality even if the form varies. Buffet might wait for a fat pitch, but when it comes the bat speed still needs to be fast.
Whatever your game, you ultimately get a feel for it by being able to hold your attention on what matters and tuning out the rest. There’s some visualization…being ready to pounce on an incorrect market that you’ve been studying. In StockSlam, you really get a sense of what consensus is for a color in a certain relative position and then your antennae is up for aberrations. You are gathering and measuring data via listening and memory while in real-life the same functions are performed in code. But they are the same functions. And both are downstream from “what do I need to be paying attention to?” That will vary by the time horizon of your strategy.
[The attendee also mentioned that the penalty for not executing the game’s “broker cards” was too low.
My response:
As far as the penalty we are actually thinking to ditch it anyway and use carrots for doing things on your card rather than punishments. But I hear you on the $5 not mattering much but it remains a useful part of the game by letting us examine if players can find the least expensive way to execute the card. You are effectively benchmarking a trade not to “does this have edge” but “is this better than negative $5”.
This is a critical concept in real life. Broadly, satisficing is often better than making perfect the enemy of the good. Also, there are some strategies that are not profitable if you have to cross a spread but are profitable if the benchmark is “it saved me from crossing a spread” (very relevant for an org that has to make many hedging trades per day). Academic papers are notorious for finding strategies that underappreciate indirect transaction costs. But you may be able to repurpose such strategies to warehouse risks instead of crossing bid-asks to shed them. That’s a lower bar than a strategy that needs to cross a spread. In a world of rebate liquidity this is especially true. The cost/rebate structures for taking/ supplying liquidity is like a 4-point swing in a basketball game.]
Related reading (as an exercise you can think of why these posts are so related to what I described above):
- If You Make Money Every Day, You’re Not Maximizing (28 min read)
- The Paradox Of Provable Alpha (1 min read)