Astral Codex Ten

ACX Book Review Contest

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ACX Book Review Contest

Article

ACX Book Review Contest is a recurring event in the Astral Codex Ten archive, appearing 2 times across 2 issues between March 27, 2022 and July 21, 2023. The archive places it in contexts such as “entries to the 2022 ACX Book Review Contest”; “see how I stack up against other ACX Book Review contest participants”. It most often appears alongside Russia, 2008 Financial Crisis, 2023 book review contest.

Metadata

  • Category: Events
  • Mention count: 2
  • Issue count: 2
  • First seen: March 27, 2022
  • Last seen: July 21, 2023

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Open Thread 217
March 27, 2022 · Original source
1: Remember: entries to the 2022 ACX Book Review Contest are due April 5th. You can send them in with this form.
Inline links: 2022 ACX Book Review Contest, this form
Your Book Review: The Laws of Trading
July 21, 2023 · Original source
We need a framework for thinking about these trades. Lebron’s first law states that we must know ourselves and our motivations for trading before we trade. We tell ourselves many stories, but someone with intellectual honesty – the person with the most alignment between their motivations and actions – will take money from the person who didn’t go through the work to understand their own motivations. There is a reason that Citadel and other hedge funds pay millions of dollars to trade with retail. They know why they are trading: to maximize profit. And the dilettante who “trades for fun” will be eaten alive by a firm with a much better model of a) the world and b) the dilettante themself. Why did I write this book review? To test my intellectual mettle. I could easily have posted this book review elsewhere, but no, I wanted to see how I stack up against other ACX Book Review contest participants. Similarly, this is often the reason people get into trading. One motivation that Lebron explicitly calls out is intellectual validation. You can toil in obscurity for years as an academic. But in trading, there is a quick feedback loop. If your P&L showed $10M last year and the guy sitting next to you showed $8M, you have demonstrated who is “cleverer” and established a clear hierarchy. What lessons here transfer to our daily lives? Like Paul Graham, Lebron encourages us to keep our identities small. He gives the standard decision-making advice to write down your framework and reasoning for why you made a decision at a specific point in time, in order to avoid biases after the fact. This section of the book contained good general advice, but nothing that will be particularly new for the median ACX reader. 2: Adverse Selection You’re never happy with the amount you traded. Now we start to get into the good stuff. Financial markets are an information aggregation mechanism, relying on multiple parties’ beliefs and recursive Bayesian updates of an individual actor’s beliefs based on the beliefs of others2. Market mechanics demonstrate Bayesian beliefs in action. The following quote is quite long, so skip over it if you don’t want to dive deep into the psychology of making a market. I retained it in full because this is quite literally the best description I’ve ever seen of the Bayesian dance between two market makers: “You are a market maker in South African mining companies. Through years of effort and continual improvement, you have built a trading model for the company Veldt Resources. You walk into work one day, ready to set up your trading for the day. It's a stock that doesn't trade much, and usually there are only two market makers: you and another (we'll call her Jo). She's sharp, and she competes well to trade against customer orders that come in. Your model has Veldt valued at 54.35 ZAR (South African rand). You're going to start quoting the stock, so you're about to turn on your machine making a market 54.25 - 54.45 (1000x)3. Before you turn on, you check the current market and notice that Jo has already turned on and she's making her market 53.50 - 54.00 (2000x). If you were to turn on your machine, your market would cross her market, and you would buy 1000 shares from her for 54.00. You now need to make a decision. Whose model do you believe more, yours or Jo's? If you believe yours, you should turn on your machine, trade at 54.00, and expect to make money. If you believe Jo's model, you should adjust your own model parameters to match her market and turn on, making a similar market to hers. What to do? As with many dichotomies, this is a false one. And as with many decision processes, Bayesian reasoning lights the way… …Jo presumably believes Veldt is worth around 53.75 (the average of her bid and offer). But how confident is she in her belief? The width of her market can give you a clue. It's 0.50 ZAR, whereas yours was going to be 0.20 ZAR wide. All other things equal, you should think that Jo only has 40% (0.20/0.50) of the confidence in her fair value as you do in yours. On some absolute scale of confidence, you can say you had a belief-strength of 100 in your fair value of 54.35 (before seeing Jo's market), and Jo has a belief-strength of 40 in her fair value of 53.75 (before seeing yours). And it turns out the weighted average of these two beliefs is quite a reasonable way to combine them: 100/140 * 54.35 + 40/140 * 53.75 = 54.18. Your updated fair value, having seen Jo's market, is thus 54.18 ZAR. This procedure is a quick, heuristic, and reduced version of Bayesian belief-updating, and a good reference on the subject is A.L. Barker's 1995 paper. After updating, you now believe that the stock is worth 54.18. Assuming your trading costs, risk limits, and return requirements are satisfied, buying 1000 shares for 54.00 is a good trade. Naively, you might just put out a 54.00 bid for 1000 shares, trade with half the 2000 share offer, and hope to collect your expected-value ZAR. In practice, however, you might be able to make even more. If Jo is making a 0.50 wide market, maybe she'd be willing to sell lower than 54.00. It's conceivable that if you put out a 53.90 bid for 1000 shares, Jo will sell at that price, and you collect an extra 100 ZAR! Of course, Jo could react differently. She could see your bid and use that information to change her market, in much the same way you did before turning on. These are difficult decisions, ones where experience with the product and the market make a big difference in being able to eke out a little extra edge. Let's play it safe however and pay 54.00 for 1000 shares. You trade, and Jo reacts by immediately canceling her market. This is not an uncommon occurrence in illiquid stocks, especially in emerging markets, so you're not too surprised. You wait a couple of minutes, mentally visualizing Jo in front of her six monitors, evaluating her trade and her model. Finally, she turns back on. Her new market is 53.50 - 54.05 (10000x)! You reason that Jo has seen that someone (you) disagrees with her valuation of the stock. Jo is a good Bayesian like you, and so she has incorporated that information into her model and updated her beliefs about the fair value of the stock. Her updated belief is that she now wants to sell even more stock, at a marginally higher price. Clearly, she almost entirely discounts the information you've communicated to her with your trade. How should you react? It seems fairly clear that, assuming Jo is not a crazy or incompetent market maker (usually a fair assumption), your trade was a bad one. You bought 1000 shares, when in retrospect, you would have wanted to buy much less, probably zero. Imagine instead that Jo had turned back on with a market of 54.00 - 54.50 (1000x). Her reaction now clearly indicates the information you gave her with your trade is valuable, and she has adjusted her beliefs accordingly. Your trade was probably a good one. Don't you wish you had bought all 2000 shares on offer? No matter what Jo's reaction is, you will be unhappy with your trade. Note that Jo will be unhappy too, since retrospectively she should have either made her initial market bigger or smaller. Welcome to the joyous world of trading!” Whether or not you make money, you have regrets! If you profited, you could have made more. If you lost money, you shouldn’t have made the trade at all. Like death and taxes, you can’t avoid adverse selection. Lebron continues to highlight a few areas of trading that have adverse selection problems. First, IPOs. If you buy the stock in an IPO, you expect the share price to “pop” on the first day of trading. However, if others also have this expectation, the round will be oversubscribed. You can only get the quantity of shares that you bid for when the market doesn’t think the shares will go up. So if you are able to get the shares that you want, the IPO is likely a dud. See also: Venture Capital fundraising. Second, powerful entities that change the rules of the game while you’re playing. Exchanges nullify “erroneous” trades. Brokerages limit buying. Anyone who tried to buy GameStop stock on Robinhood on January 28, 2021, knows this form of adverse selection all too well. Lebron also highlights “special trades”, in which you should throw the “normal rules” out of the window. This advice generalizes to other areas of life: “The normal rules do not apply. If you remove yourself from our usual routine, if you think hard and clearly about the specific situation, maybe you can do something good. Perhaps even great. Others will be paralyzed by inaction, but perhaps you won’t be. Crises can be opportunities.” 3: Risk Take only the risks you’re being paid to take. Hedge the others. In trading, as in life, you can make the right call in expected value terms but still lose due to randomness. Some of that randomness is avoidable. Some of it is not — and can be accounted for by hedging. Here, Lebron encourages us to rely on multiple risk measures and actively seek to understand the risks that we might be subject to. That’s all well and good in the world of finance, with derivatives contracts. But how might this apply in other areas of life? If you work for a publicly traded company and are compensated in stock, sell your shares as soon as you receive them. This is not because I don’t expect the share price of Microsoft/Meta/Apple/etc. to go up. The stock may very well outperform the market. But you are not being compensated for the added risk that you take on here. Your employment prospects at Microsoft/Meta/Apple/etc. are highly correlated with the share price. When the share price is down is when layoffs happen. Former Enron employees can chime in here. Similarly, it makes sense to hedge anything that is outside of your control. Let’s say you’ve decided the crypto bear market of 2023 is a great time to start a new crypto company. Your success depends on things within your control, such as: Your idea
Inline links: pay millions of dollars to trade with retail, keep our identities small, 2, market makers, 3