The poker face of Wall Street

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1. kqrAug 2021
   I accidentally wrote this comment backwards and I'm on a device that doesn't let me edit very well. You'll have to put up with some nonsense drivel until you get to the part you actually asked about.

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   I don't have much advice on that, specifically, other than

   1. Be very careful. When shit hits the fan and you need that independence most, all correlations go to 1.

   Related to the above, understand the cut-throat nature of the business. Actually, reading The Poker Face of Wall Street by Aaron Brown might help with this. The difficult part is not winning big; the difficult part comes after that, when you need to convert people's promises into money and successfully walk out with it.

   Nobody is looking out for you. It's nobody's job to make sure you get what you are supposed to. I bring this up because I see people spend lots of time concocting advanced technical strategies but then forget the people skills required to get away with it.

   2. Learn and play around with as much statistics as you can find the time to. I like reading actuarial textbooks because many of them work in insurance which means they do specifically that.

   3. I'm a big fan of Aaron Brown for putting into simple words many of the most important concepts. Read Risk Management for Dummies (honestly!)

   In particular, once you start making specific bets, you don't want to have stop losses that prevent you from losing too much money. That's a sure way to lose money. Instead, think like a scientist. Do hypothesis-based trading. "I think that X, because Y. If I see signal Z from the market, one of my assumptions are invalidated and I will reject my hypothesis and exit the trade."

   4. Set aside a small amount of money to practise with. Try to invest in ways that expose you to specific spreads, like big vs small market cap narrowing, or service vs product sectors widening.

   Try to make these actual bets you believe in. The point of this exercise is that you will, at a low cost, see how often you're wrong and go back to a plain constant fraction rebalanced portfolio, with a small but solid mix of high and low volatility assets.

   It's really hard to do much better than that.

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   Edit: upon re-reading your question, I think I misinterpreted it. Maybe you're just asking about how to find a small but solid mix of high and low volatility assets to have in your constant fraction rebalanced portfolio.

   One start is reading Thomas Cover's 1991 paper on the Universal Portfolio. In order to understand that, however, you might need to back up a bit more into the history of E log X optimisation. The book "Kelly Investment Criterion for Capital Growth" is a collection of historic and modern papers on E log X, including Cover's. It's a very good book. Though it takes a bit of working through results practically to understand.

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   That will help you find a decent mix of high and low volatility. Specific numbers are no big deal, though. I would recommend at least 50 % low volatility, but some people (like Taleb) suggest up to 90 %.

   The mix of high volatility is also not that sensitive to specific numbers. To avoid having to do difficult optimisation, just split evenly between whatever assets you want in there.

   To avoid correlations, just look for things that seem like they're not cointegrated. There's no point in optimising this too hard either, because as I started out saying, when you need uncorrelated returns, they will be correlated.

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2. lordnachoJul 2021
   I've worked as a quant/dev in finance for nearly two decades now, and I've been fortunate enough to touch every major asset class on multiple time scales.

   You have to start with the fundamentals, on two abstraction layers. The two layers are the financial abstractions (instruments, entities, ecologies) and technical ones (programs, networks, databases).

   Since you're asking about the markets, let's leave the technical ones for now. They're a whole universe in themselves.

   The fundamentals of instruments are things like the time value of money and optionality. Read Hull and Willmot, and maybe Natenberg. Whether you're a Venetian banker during the Renaissance or staking the newest, fanciest cryptocurrency, these books will illuminate how the instruments work. They are technical books, not so narrative as much as math. But from a few building blocks you will be able to understand how all sorts of things work, eg convertible bonds, rights issues, dividends (this is a surprisingly insanely deep rabbit hole), interest rate swaps, mortgages, ETFs, employee option grants, stablecoins.

   The narrative perspective is absolute useful too. It's more reading per nugget, but Reminiscences is one of those things that shows you how regardless of how thing are done technologically (boys running around vs computers) there's a way the ecology works with the participants' incentives. There's also the stock market wizards books, interviews with mostly macro traders IIRC. Ed Thorpe's memoirs are pretty good at tying a certain kind of mathematical thinking to the markets. The Renaissance Technologies book that recently came out is excellent too. Poker Face of Wall Street, another one of those about connections between games and markets.

   This would provide a grounding in being able to understand the kind of stuff Matt Levine writes, he writes good stuff but it's so diverse that without a foundation it just feels like a whole bunch of different things.

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3. kqrFeb 2021
   I personally think a lot of established macroeconomics is bullshit. There's just no mechanism to verify many of the hypotheses, and practitioners rarely suffer from the kind of personal survival pressure that otherwise tends to filter out for people who are right.

   Not to mention that the entire economy is such a complex system, and my experience with complex systems is that we can't predict them, we like to come up with causal explanations for observed events, and we're almost always wrong on closer scrutiny.

   With all that in mind, I have started my economics journey not on the macro picture, but on the details. Book recommendations include:

   - The Kelly capital investment criterion: a collection of historic peer-reviewed papers about what it says on the tin: how to allocate resources to risky ventures (read: how to size bets.)

   - Red-Blooded Risk: a wandering tale about quantitative risk management and how it entered the world of finance in the seventies--eighties. As always with Aaron Brown, it contains lots of information on economic matters that aren't directly related to the main subject.

   - The Economic Function of Futures Markets: a correct, for once, exposition on how futures markets are not about locking in prices (any regular contract can do that) or hedging (the people who supposedly would hedge don't) but about creating an implicit loan market for commodities, among other things.

   - The Poker Face of Wall Street: a wandering tale on the similarities between betting, speculation in financial instruments, and insurance, among other things.

   - The (Mis)Behaviour of Markets: Benoit Mandelbrot summarises some of his research into modeling markets with multifractal geometric ideas.

   - Fortune's Formula: a more pop-sci friendly version of the Kelly criterion paper collection.

   - Regression Modeling with Financial and Actuarial Applications: basic techniques used everywhere for statistical modeling of things.

   - Moneyball: finding not the best, but the most undervalued through quantitative reasoning.

   - Inadequate Equilibria: a framework for thinking about when economic incentives align with a desired outcome and when they don't.

   I have also started a fairly advanced prediction market at work to get a better sense for how such things work, and I'm the guy who you either love or hate playing Risk and Monopoly with, because I invent derivative money and all sorts of exotic contracts as an aid to diplomacy.

   But then again, I generally build knowledge by generalizing from specific concrete experiences. Maybe that bottom-up approach works badly for some people.

   Edit: I should say that these are some of the books I have read and can personally vouch for. There are several more like them in my stack of books to read. I can list some of those that I think are more promising, but I can't personally vouch for them yet.

   Second edit: oh, I almost forgot some of the most important parts. I don't have a specific reference, but double-entry accounting and financial reports are things that will teach you a lot of the basic terminology about assets, liabilities, equity, credit and so on.

   I suggest maintaining your personal (or your family's) books with double-entry. A great pplace to start is Plain Text Accounting.

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4. lordnachoMay 2018
   The Poker Face of Wall Street by Aaron Brown

   Tied together a lot of concepts from my undergrad with my new work, and on top of it also Poker, which became one of my main analogies for life.

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5. jcardenFeb 2012
   The Theory That Would Not Die (http://amzn.to/w0N2Q6)is a non-technical and nice historical overview of Bayes theorem and its applications. Another one I've enjoyed recently is Evening's Empire: A History of the Night in Early Modern Europe(http://amzn.to/zywusB). I started the Big Short too, but just couldn't really get into it. If you like things along that line then you should check out The Poker Face of Wall Street (http://amzn.to/zcbYHy)
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6. vmOct 2017
   Black-Scholes was prescient went it first hit the scene in the late 60s/early 70s. Ed Thorp (who independently derived the formula before B-S published it) made tens of millions of dollars applying it through his statistical arbitrage hedge fund. It was the most accurate predictor at the time.

   It's also remarkable that four people independently derived the formula: "In coming up with a trading strategy for warrants, Ed discovered a handy formula. A few years later, three finance professors independently came up with their own slight mathematical variant of the same formula. Ed Thorp, Myron Scholes, Robert Merton, and Fischer Black all had almost the same formula, but each had a different reason for believing it was true. Ed showed that it was a way to make money..." Source: The Poker Face of Wall Street (Wiley 2006)

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