Probability and Markets [pdf]
janestreet.com
janestreet.com
http://www.jdawiseman.com/books/pricing-money/Pricing_Money_...
> The price is a review (even if only a few words) on your preferred social media, either tagged #PricingMoney or with the link jdawiseman.com/PricingMoney.html. There is no paywall, nor registration, nor even cookies, so this price cannot be enforced. Nonetheless, please be fair: please post a review or comment or acknowledgement, tagged or linked or both. Thank you.
If you look through this PDF it does not even cover a 1.semester course in probability theory from a bachelors degree in CS/math.
I always go back to this lecture series to brush up my fundaments. https://projects.iq.harvard.edu/stat110/youtube
If you want books about trading mathematics and interviews in general, the green book (practical guide to quant finance interviews by Zhou) is the usual go-to. Falcon's Heard On the Street is another classic. Note both of these focus on generic interviews (the quant equivalent of leetcoding) usually for recruiting fresh grads directly out of school. You can probably skip some steps if you have the work experience or know the right people at certain funds.
If you want actual books on trading and modeling, that's another topic entirely.
Figure out which area specifically you are interested in: https://teddit.net/r/wallstreetbets/comments/kcs4xy/what_qua...
If you want an autobiography, Emanuel Derman's My Life as a Quant is well-regarded. I have never read it myself but Amazon has often recommended me The Physics of Wall Street.
I also have to say that you don't need to have a strong intuition for differential equations even through they are very related. Stochastic calculus can be studied entirely in its own right.
And with respect to quantitative finance my experience is that while a candidate knowing about or having coursework in stochastic calculus is certainly relevant its not favourable.
This also depends on the exact job right, if you have to be pricing XVa products then you better have know Girsanov, Ito and what have you =)
Slightly off topic, but how do you feel about his “story proofs”? They were the one part of his lectures that never really clicked with me, but I never talked to anyone else about it.
I took the course in person, so it might have been more understandable in that context.
This is similar to how math Olympiad students are picked, most questions involve combinatorics and Euclidean geometry instead of advanced calculus, as anyone can understand a basic counting problem, but it’d take a few semesters to define the terms in a typical calculus problem.
Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications by Nassim Nicholas Taleb
Beware that it may be way more advanced than what you'd expect though.
The non-technical introduction chapter is pretty easy to follow, and I would recommend reading it.
Assuming you have some freshman-college math literacy, here are a few great introductory books to Probability theory:
Introduction To Probability by D. Bertsekas & J. Tsitsiklis
Elementary Probability Theory: With Stochastic Processes and an Introduction to Mathematical Finance by K. L. Chung & F. AitSahlia
An Introduction to Probability Theory and Its Applications Vol. 1 by W. Feller
I'm sure there are some great video lectures on the subject as well, but unfortunately I can't point you to any relevant material since textbooks is what I used when I was in college. But if I had to guess, the latest relevant MIT OCW course that has video lectures available would be sufficient.
We tend to gloss over assumptions like finite variance or iid but they matter a lot for reasoning correctly on fat tailed distributions.
Also the law of large numbers only works if you don’t have a game over scenario or fat tails.
https://www.amazon.com/gp/aw/review/1544508050/RMD3OUG0WQWWY
HN thread: wHeRe is compleXitY?!?
Their discussion of die rolling doesn't say they can predict the outcome, rather that they can predict the probability of the outcome. In trading, life, and gaming that probability is one component of many that go into decision making. Knowing how much that randomness effects the outcome at what probability can be a successful strategy (but of course not guaranteed) in all of those domains.
It is, but that doesn't mean you couldn't predict it with perfect information. A chaotic system is roughly speaking one that is
- sensitive to initial conditions, in the sense that the distance between nearby trajectories in phase space grows as e^{l*t} for time t and some positive number l
- mixing, meaning that given any two open sets in phase space X and Y there's at least one trajectory from a point in X to a point in Y.
and so if you have any error bounds at all on your measurement of the initial state (which of course you always do) then you can't predict where it ends up in the long term. But there are plenty of chaotic systems for which exact numerical computations are quite simple. The logistic map x_{n+1} = rx_{n}*(1-x_{n}) for instance, is chaotic for many values of r.
> We care about how you think about and approach tough problems, which includes how you take hints and respond to being wrong.
First and second sentences need to swapped.
It's telling that Jane Street stayed away from the whole mess despite the connection.
They only decided to “step back” now that regulators are bringing the hammer down
This wasn’t an innocent bystander firm. They were complicit, either directly or indirectly
How is that different from bragging that you finished MIT, Harvard, Oxford, you worked at Google or that your startup is in YC.
JS aren't future tellers when it comes to their employees' lives after they leave JS, and JS has no control over those employees once they depart either.
https://web.archive.org/web/20230000000000*/https://www.jane...
If i were a hedge fund recruiter, here is what I would do:
Go on /r/ wallstreetbets and find guys who consistently are pulling huge $ like this guy:
https://old.reddit.com/r/wallstreetbets/comments/14a9xyi/11m...
Send a DM job offer to all of them, 7 figures plus performance comp , or at least an interview that will be paid anyway, so as to not waste the recipient's time.
Why ask brain teasers to try to find skills that correlate with trading when you can just pick out the best traders who already have demonstrable skill? that is how sports recruiting works. they find the people who can play the sport at best level.
WSB is a gambling forum where people are addicted to chasing huge rewards by taking huge risks.
This would be about as interesting as going to a casino and recruting people who have a big win streak at the slot machine.
Opening sentence: The goal of this document is to present an introduction to probability and markets in what we hope is a fairly intuitive and accessible way.
They have a whole page dedicated to that: https://www.janestreet.com/join-jane-street/interviewing/
Attention to detail and clear communication, including good grammar and spelling, are probably important.
Ask people to predict whether the Jones' have a dog and they'll start asking questions about the specifics of the Jones', like do they have children, do they live in the suburbs, have they always wanted a dog, etc. Then they try to construct a narrative based on "logical" conclusions.
When the right approach might be to ask "what's the background rate of dog ownership in the Jones' country of residence?" But that takes ignoring the specifics, and that doesn't come easily to people. It took several millennia for anyone to realise it could be useful.
This is the core idea of Bayesian statistics.
You fall back to the background rate only if you have no ability to access more pertinent facts.
The difference between Bayesian reasoning and narrative fallacy is a coherent evaluation of joint probabilities -- the bit most people skip over.
But why do you point the finger at narrative instead of a simple lack of data? In the dog example, those questions are absolutely good ones to ask, so long as the data can be found.
One reason to reject this mental opposition between 'Bayesian reasoning' and 'narrative fallacy' is because narratives are essential to coming up with good conditions on which to split the population in the first place.
'I would ask whether they have children, because children like dogs and therefore it's plausible that families with children have a higher rate of dog ownership' is a reasonable narrative that justifies collecting an extra column in your dataset.
Granted, one shouldn't accept that story uncritically without checking against the actual statistics. But narratives themselves are unavoidable even when you're doing statistics correctly!
You see this all the time in sports betting, too. People overcorrect from the base rate when receiving news. People love a narrative that makes sense to them, but statistically it rarely holds up against statistical reasoning with a very small set of variables. (For more examples, see Meehl's research around clinical vs actuarial judgment.)
Yes, you're correct that a hypothesis is one of those narratives, but if you're operating at that level of scientific abstraction you can ignore my comment. Most people don't, and just roll with the story and forget about the probabilities.
You're right that some education would help people with everyday situations. It'd be nice if schools had intro to probability and statistics as the only required math course for all students. Even just covering fractions for probabilities, mean/median/mode, and then memorizing basic tricks or terms to look up on Wikipedia/Wolfram throughout life.