not getting picked off

Moontower Munchies #78

Sep 04, 2024

Friends,

A reader asked a question in relation to this excerpt from Why Home Prices Could Fall With Mortgage Rates:

I wrote Staring Out The Window in October 2022:
Musing #1: Bid-Ask Widening
A year ago the people that paid ridiculous prices for RE were market orders. “Fill me at any price”. Many of them were immediately in the money (ie they probably could have turned around and sold a month later for more. Maybe not net of transaction costs but you get the idea). This isn’t shocking. When optimism turns to euphoria, the rate of change of the returns themselves can explode into a parabolic curve. Of course, such curves are unsustainable. The smug moment of being in the money is short-lived in the same way that a fund that buys a ton of stock going into the close usually gets a favorable mark on their daily p/l. Their sloppy buys drove the price higher in a short period of time. The real sellers didn’t have time to react before the close. But as soon as they check the comps overnight, you can be sure the supply is coming tomorrow morning.
I think of it like water going down a drain…once most of the water is through the drain the remaining liquid swirls quickly around the drain before you hear that sucking sound. Whoosh. The last bid is filled. With maximum punnage — the liquidity is gone.
In the meantime, many other buyers were priced out. You can think of them as limit bids. It’s an imperfect analogy but it will suffice. As things go south now, some of those bidders might be anchored to their original bids which were “cheaper” than where the home traded. However, if they get filled on the way down, they actually have more negative edge even though they got this theoretical house for a cheaper price than the original buyer. You could belabor this with a stylized model but understanding this concept is a big step towards understanding trading.

The question:

Can you elaborate more on what you meant when you said, “However, if they get filled on the way down, they actually have more negative edge even though they got this theoretical house for a cheaper price than the original buyer. You could belabor this with a stylized model but understanding this concept is a big step towards understanding trading.”

Getting filled later on your lower bid is due to the market changing and your original bid being stale and anchored to the prior conditions. In trading, the question is always -- I think I have X% edge but what is X conditional on getting filled? Think of it this way: When a stock is trading for $50 and you have a good-til-cancel bid of $49...do you think you will be happy when you finally get filled? I know it feels good to imagine buying for $49 when a stock is trading $50. It feels different when you finally get hit.

In A Jane Street Alum Teaches Trading, Patrick McKenzie asks Ricki what the most important lesson you learn from trading. She says adverse selection. What’s my edge conditional on getting filled? The interview bangs you over the head with the idea which reflects its real-world weight in considering trades — I spent every waking second wondering why I “get” to buy or sell at price X. Here’s how she teaches it on day one:

While trading is still open, I have each person in the room come up and write the number of siblings that they have on the board one by one so that we are calculating the sum of the number of siblings that we have, combined, in real time. Trading is still open and people are continuing to trade, but they’re updating their models as each new number gets written.
And this allows me to say things to people like, “Hey, somebody just wrote a seven up on the board. This presumably updates your value for the true sum of the number of siblings in this room upwards. What is the first thing you want to do?”
Usually the first thing that people in the room do is go, “whoa,” and then they look at the trades that they’ve already done and try to figure out how much money they gained or lost as a result of that. For this, I chastise them. I say the absolute first thing you want to do is going to be orienting toward the order book. You want to be staying out and clearing all the orders that you have that might be stale, even if you don’t remember whether or not those orders are good, even if you don’t remember what side of the order book they’re on.
The first thing that happens is, you have new information that markets are different from what you thought, and the fastest thing for you to do to protect yourself is to be out on the stale orders of yours that are now stale to new information.
The next thing you want to do is to go in the direction of the new information’s indication. And you want to be doing this to approximately the right order of magnitude.
In terms of how much you move the price, if you see a seven, that’s a big surprise relative to a one, which might be your expectation of how many siblings someone has, such that if the spread had been one wide or two wide, you should be happy lifting the offer even if you weren’t paying attention beforehand to what your model says the exact sum should be.
The conservative way to approach things is anytime there’s new information and your model shifts, you should be extra paranoid about the orders that you have that are still posted to the book, and you should be rushing to clear those orders, or to say out on your orders so that they’re removed from the order book, in particular because of the concern we talked about earlier of adverse selection.
If you still have orders on the book, let’s say those orders are good to the fair value, i.e. you would be happy for them to be traded with, people are not necessarily going to trade with them because they don’t want to do bad trades.
But let’s say your orders are stale in the direction of being bad. Somebody is going to come in, see that, and trade with it, if they can do that faster than you can clear your order. It is more efficient for you to clear your orders, than for you to recalculate what you think the new fair value is based on having now added in that person’s seven siblings, subtracted them out from the number that you multiply by your expectation of the average number of siblings, make sure you’ve counted how many people have already written, come up with a new number and decide if your order looks good to that number.
A lot of the thing that I’m trying to convey in the trading curriculum as a whole is that, to be a good trader, you don’t necessarily need to be the person to get the exact right number after many minutes of painstaking equations and double checking every single odd constant that gets added to the end of that equation.
You need to be the fastest, you need to be going in the correct direction, and you need to have some sense of approximately how much you think the price of this asset should move, or how much you think the price of this stock should move. Those things need to come first because if you are the first trader, it is possible that you will get a good trade. If you are the 10th trader, it is way less likely because someone of the first nine traders was able to do the overwhelming majority of the good trades and take them out from under you.
You are looking to maximize in dollar terms to make as many dollars as possible, and in order to do that you need to be fast. You need to be fast because, if you are going slower, it is more likely that your model will have mistakes conditional on getting filled, even though your model now feels like it is so much more well thought out and more likely to be correct, if you weren’t then also conditioning on that fill.

This is classic mock trading — teaching people to yell “I’m out” to cancel bids/offers that are stale when news comes out. Also the importance of remembering who has a bid/offer dangling so you can pick them off. In Fantasy Football, it’s like sitting on the waiver wire page on a Sunday waiting for injuries (if you are in a crackhead league that doesn’t lock waivers when games start).

[In that interview, Ricki also explains how order types can reveal how much adversity an order might contain or how to discern adversarial from cooperative environments.]

More examples of “what’s my edge conditional on being filled?”

1) Posting vs taking liquidity

From Reflections on Getting Filled:

My Bayesian analysis of being filled on a limit order vs market order
Imagine a 1 penny-wide bid/ask.
If you bid for a stock with a limit order your minimum loss is 1/2 the bid-ask spread. Frequently you have just lost half a cent as you only get filled when fair value ticks down by a penny (assuming the market maker needs 1/2 cent edge to trade). But if you are bidding, and super bearish news hits the tape (or god forbid your posting limit orders just before the FOMC or DOE announce economic or oil inventory), your buy might be bad by a dollar before you can read the headline.
If you lift an offer with an aggressive limit (don’t use market order which a computer translates at “fill me at any price” which is something no human has ever meant), then your maximum and most likely loss scenario is 1/2 the bid-ask spread.
Do you see the logical asymmetry conditional on being filled?
Passive bid: best case scenario is losing 1/2 cent
Aggressive bid: worst case scenario is losing 1/2 cent
This is why exchanges offer rebates for posting bids/offers — the payment incentivizes liquidity which nobody would ever offer otherwise because of adverse selection concerns. When you are not a market-maker you have the luxury of “laying in the weeds” until you spot the “wrong” price and then strike.

2) Winner’s Curse in auctions

I always liked quant investor Chris Schindler’s example from private markets. From Recipe For Overpaying:

On the Gestalt University podcast, Chris Schindler has an intuitive explanation for the CAPM-defying empirical result that says higher volatility assets actually exhibit lower forward returns. Very simply explained — a large dispersion of opinion leads to overpaying. He points to private markets where you cannot short a company. The most optimistic opinion of a company’s prospects will set the price.
Options markets don’t care about CAPM. They model geometric returns. Higher volatility explicitly maps to lower expected geometric returns. I’ve referred to this idea as a “volatility drain” before. But here’s another way to see this. If you hold the price of an asset constant and raise the volatility the median expected outcome is necessarily more negative. Why? Because a stock is bounded by zero, so increasing the volatility should seemingly make the expected value of the asset higher. But if the market thinks the stock price is worth the same despite the higher volatility, that implies the probability of the asset declining must be higher.
If you want to fetch a high price for an asset, you want its value to be highly uncertain. Then sell it in an un-shortable auction with many bidders.

In Math Games With Bad Drawings, Ben Orlin gives you the rules to an auction game you can play right now called Caveat Emptor:

I'm afraid I can't teach you how to win at Caveat Emptor. But I can easily tell you the best way to lose: Just win every auction. I mean it. Play a few rounds, and you'll find that overbidding is all too common. It's a game marked by Pyrrhic victories, with winners forced to take home prizes for more than they're worth. This phenomenon-losing your shirt on a winning bid is so pervasive that auction economists have dubbed it "the winner's curse."

Lucky for you, Caveat Emptor offers a lot more information than the typical auction. Will that be enough for you to escape the curse?

WHY IT MATTERS

Because everything has a price, and auction winners often overshoot it.We live in a world on auction.

Photographs have been auctioned for $5 million, watches for $25 million, cars for $50 million, and (thanks to the advent of non-fungible tokens) jpegs for $69 million. Google auctions off ads on search terms, the US government auctions off bands of the electromagnetic spectrum, and in 2017, a painting of Jesus crossing his fingers fetched $450 million at auction. Before we dub this the worst-ever use of half a billion dollars, remember two things: (1) The human race spent $528 million on tickets to The Boss Baby, and (2) it's a notorious truth about auctions that the winner often overpays.

Why does this winner's curse exist? After all, under the right conditions, we're pretty sharp at estimation.

Case in point: In the early history of statistics, 787 people at a county fair attempted to guess the weight of an ox. These were not oxen experts. They were not master weight guessers. They were ordinary, fair going folks. Yet somehow their average guess (1,207 pounds) came within 1% of the truth (1,198 pounds). Impressive stuff. Did you catch the key word, though? Average. Individual guesses landed all over the map, some wildly high, some absurdly low. It took aggregating the data into a single numerical average to reveal the wisdom of the crowd.

[Kris: From Superforecasting:

How The Wisdom of Crowds Works
- 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.
- Aggregation works best when the people making judgments have a lot of knowledge about many things.
- Aggregations of aggregations or “polls of polls” can also yield impressive results. That's how foxes think. They pull together information from diverse sources. The metaphor Tetlock uses is they see with a multi-faceted dragonfly's eye.
Now, when you bid at an auction — specifically, on an item desired for its exchange value not for sentimental or personal reasons — you are in effect estimating its value. So is every other bidder. Thus, the true value ought to fall pretty close to the average bid. Here's the thing: Average bids don't win. Items go to the highest bidder, at a price of $1 more than whatever the second highest bidder was willing to pay. The second-highest bidder probably overbid, just as the second-highest guesser probably overestimated the ox's weight.
To be sure, not all winners are cursed. In many cases, your bid isn't an estimate of an unknown value but a declaration of the item's personal value to you. In that light, the winner is simply the one who values the item most highly. No curse there.
But other occasions come much closer to Caveat Emptor: The item has a single true value which no one knows precisely and everyone is trying to estimate.

Adding a few additional thoughts

Traders have all seen it…a large offer “at the figure” gets filled. You knew the stock would rip higher higher once it’s filled but you are waiting to lift the last bit. But of course I’m dating myself, because you can’t beat the chipper algo when it knows the best time to sweep the balance. (But then the seller knows how this goes so they only show size on the offer when they have a lot more re-loads behind and the offer is part offer and part advertisement).

“Adverse selection” — the word is right there — “adverserial”. The cat-and-mouse multi-order poker game is a never-ending bowl of spaghetti (mixing 3 metaphors in a sentence is proof that GPT didn’t write this. Even a writer trained on internet mid-ness knows better).

Markets constantly learn. If you want to keep solving new puzzles it’s a great career. If you want to build enterprise value by repeating the same things while not living in second-to-second paranoia this industry stinks. The pressure to evolve and reinvent is tangible.

But I also think that’s why trading firms are epistemology machines. That thought became very clear from a different Patrick Mackenzie interview. He talks to another Jane Street alum, Zvi Mowshowitz, in a conversation that is broader than just trading (although you certainly get insight into Jane Street and the world of sports gambling). What stands out is how hedge funds were much better at modeling Covid than doctors or the government.

Finding signal in noise is the abstract definition of finding alpha. A massive financial incentive induces competitive pressures in zero-sum markets. The alpha-finding apparatus of data cleaning, crunching, and inference is an all-purpose technology that would drown an expert who showed up armed with an anchor of fixed knowledge around their neck but who is slow to update.

Strategically, it’s probably a career mistake to have your value depend on expertise in a realm where the cost to acquire and process data (plus the compute costs) into signal is falling faster than the rate at which you can capture monetizable relationships with your expertise/status.

Time, if it hasn’t already, is unmasking the cultural divide between the hackers whose power grows with technology and the clerics presiding over the remains of slow-moving institutions. In a simple view of the world, there are 2 ways to gather resources. Make risky, well-calibrated decisions that outmaneuver inferior bettors (bettors as a stand-in for any decision) or persuade power to reject the entire framework of mapping rewards to good predictions.

[The authoritarian impulses of the far right and far left both espouse such an untethering of cause and effect. The unified position is both sides want to pretend that everything they enjoy could have happened without the things they don’t.

When someone wants to “make America great again” they aren’t suggesting we go back to rotary phones. And yet much of what bothers them is finding out how people next door live. Come to think of it, the technology known as Nextdoor is a striking metaphor for this.

And when one tweets from a lithium-powered 4.7 inch phone/camera/computer in their hand to tax unrealized gains, he doesn’t see the irony.]

I’ll leave you with a final section from Ricki’s interview:

Why people who have the best model for the world may or may not make the most money from trading.

I actually think in markets like these (ie the siblng market game), where there will be a settlement to the correct value, you’re more likely to make money by having good models than you will in markets like for various stocks in the US equities markets, where a lot of what you’re doing is trying to price things relative to what people in the market will think something’s worth than to a model that takes into account e.g. the earnings reports of a company and figures out what the actual value proposition of the product that they create is.

You are much more interested in what the directions that these prices move within the next few minutes or within the next few days will be. This also exists in a smaller form in the markets that I run that might resolve on the order of half an hour from now, where if you can notice what trends will happen in the next 10 minutes or what trends are already emerging, you can profit by buying and then selling or by selling and then buying a contract that doesn’t actually accumulate a position that you will get paid proportional to at the end, but instead, does what’s called flipping a contract, where by taking both legs of the contract, you can make money on the difference between those two prices.

[Kris: In the StockSlam game sessions we ran there was no private info and the color race was random. However, some players would follow a strategy of hoarding a color because if it won it guaranteed them victory. To be clear the strategy has zero expectancy. However, if you just try to “flip” for edge you probably won’t win — you’ll lose to a a random hoarder. The key is to understand that over the course of many games, the “flipper” who makes positive expectancy trades will win over time even though they never win any single match! In StockSlam, there was no way to have an “investing” edge by having a better model of the world since it was random but there were many relative value scalp opportunities.]

Stay Groovy

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