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Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

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Re: Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

#61

Post by FabHK »

madars wrote: post #31
Workaccount2 wrote: post #4

I will eternally find it sad how much talent is wasted on trading. So much money, so much intelligence, so much time and effort, all the provide almost no tangible value to society.

Why would it be wasted? I like apples and I like being able to buy them any day of year, even out of season. The only reason I can do so is because there is an entire industry of people who manage the inventory, distribution, try to predict supply and demand, and take on a risk doing that. The principal reason why I can buy and sell equities at very small spreads any day of the year is similar: traders are competing…

Sure, some derivatives have some utility.

But, ok, Spread Networks spent $300m around 2010 to build a somewhat straighter glass fibre data connection between NY and Chicago. Other companies spent more to build a micro wave connection, since microwaves move faster through air than light through fibre.

Is that money really well spent? And I'm afraid a lot of what happens in finance is similarly private gain, without much social welfare increase.

Re: Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

#62

Post by EGreg »

Workaccount2 wrote: post #4

I will eternally find it sad how much talent is wasted on trading. So much money, so much intelligence, so much time and effort, all the provide almost no tangible value to society.

It's a zero-sum game. People don't understand this: https://community.intercoin.app/t/intercoin-compared-to-the-...

I guess advertising is too, in the limit. So a lot of the business model on the web is also not much value to society.

Re: Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

#63

Post by foobar10000 »

YZF wrote: post #26
foobar10000 wrote: post #21

Earlier quoted context omitted.

While there's many definitions, I'd concentrate on zero-sum vs non-zero-sum games. Lots of games in trading are effectively zero-sum games - if I make 100USD, you lose 100USD (there's details about transaction costs going to exchanges that make this more nuanced, but the principle applies). A chunk of financial engineering games are not : for example risk pooling games. A big part of finance for example is liquidity…

Financial markets are not zero sum. Wealth is created or destroyed in financial markets despite each trade having a buyer and a seller.

I mean - of course the entirety of financial markets are not zero sum - it would indeed be almost lunacy to claim that :) My point is that there's a big part of the financial markets that _are_ zero-sum - not that all financial markets are. One can argue about EMH and that the zero-sum games are in fact injecting information into the market by providing better price discovery, and that is indeed an argument - but one is left with the intellectually unsatisfying statement of "Well, anything the market does is information extraction, and the market is an information extraction machine, so prima facie, it works" - which is basically restating the EMH axiom.

For example, the added value provided by sub-millisecond arbitrage between NY and Chicago - while making the prices converge a fraction of a millisecond faster, makes the overall set of people playing that game in excess of 1B USD in aggregate. I'd argue that such a ratio of profit vs value-added gain is very bad, bordering on 0 - thus making that effectively a zero-sum game.

Re: Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

#65

Post by rtpg »

Barrin92 wrote: post #24
bblcla wrote: post #16

Earlier quoted context omitted.

Tyler Cowen did an 'ask me anything' at Jane Street when I interned in 2016. One of the interns asked him exactly this: "What do you think of the fact that we all work here instead of, I don't know, curing cancer?" He replied with, roughly, "Those of you who work here probably couldn't do anything else other than perhaps math research. Arguably, working here is the economically efficient use of your time." I think ab…

General intelligence is overstated sometimes, but it is a thing. Someone who is smart enough to work for Jane Street probably could at least be an intelligence analyst or software developer at the NSA contributing to national security. (Jim Simmons literally was a code breaker during the Vietnam War) I don't think there's a gene for playing esoteric minigames on the options market while you literally suck at everythi…

While I’m sympathetic to the “people working hard to build out ad markets instead of cancer research” argument, I only believe in a weak version of this.

Why? I have met many “smart with computers” people. Many of them have terrible people skills, don’t show up to things on time, are unable to keep their workspace clean, don’t know how to explain anything, and cry about how they can absolutely never ever be interrupted because their workspace is so hard. There are also people who are “good at it all”, of course, but I have the impression that the math/computer people tend to be fairly unwilling to deal with even mild inconveniences.

People who can barely deal with the tyranny of daily standups probably would struggle a lot in a world where you need to write grant proposals continuously to justify your existence.

I’m being glib for effect, but there’s so much involved in getting work done beyond “being smart”!

Besides… it’s not like the reason we don’t do more cancer research is because smart people didn’t go into that. “Cancer research” is limited by funding for positions into that domain!

So “this quant should have been a cancer researcher” is saying “this person who decided to become a quant will be a better cancer researcher than a cancer researcher who went into that domain directly”. I don’t know the prestige vectors there but it’s a stretch in my book!

Re: Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

#66

Post by laidoffamazon »

ecshafer wrote: post #15
laidoffamazon wrote: post #3

> In Young Cho thought she was going to be a doctor but fell into a trading internship at Jane Street Genuinely perplexing how they always try to show each multi-million earning engineer as some normal person and not someone that went to Exeter and Harvard

Graduating from a SUNY school, Jane Street gave me an interview and a fair shake. I didn't get the sense of elitism there. There does happen to be a lot of really smart engineers, mathematicians and scientists going to Harvard, MIT, etc. There are places that without that Ivy League or Target School degree you don't hear back, I don't think Jane Street is one of them.

Might be different with a SUNY PhD or another trading firm on the resume.

I don't think they'd ever call someone like me back, not that I'd ever be able to pass a coding round with them.

Re: Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

#67

Post by laidoffamazon »

foobar10000 wrote: post #7
laidoffamazon wrote: post #3

> In Young Cho thought she was going to be a doctor but fell into a trading internship at Jane Street Genuinely perplexing how they always try to show each multi-million earning engineer as some normal person and not someone that went to Exeter and Harvard

I don't really see a lot of elitism in these circles - there's a chunk of such engineers that did not in fact go to Exeter and Harvard - but what they have in common is they are all to a fault very bright technical people that can produce complicated things quite quickly, and can communicate effectively with people around them - to make sure that what they produce is in fact useful :)

I genuinely don't think people that went to Exeter and Harvard (or MIT or Stanford or Penn or other JS feeder schools) see the rest of us as human unless there's another common factor like being employed by Jane Street and having those quirky hobbies the rest of them do - and then they rationalize it away by saying that the exceptions didn't apply to top schools, or were admitted but didn't attend for "financial reasons".

People like me are "NPCs" in their parlance.

Re: Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

#69

Post by FabHK »

fireburning wrote: post #28
foobar10000 wrote: post #7

Earlier quoted context omitted.

I don't really see a lot of elitism in these circles - there's a chunk of such engineers that did not in fact go to Exeter and Harvard - but what they have in common is they are all to a fault very bright technical people that can produce complicated things quite quickly, and can communicate effectively with people around them - to make sure that what they produce is in fact useful :)

ever meet any such engineers from the bottom 20% of the world population? just some food for thought :)

You can levy plenty of criticism at finance. But they certainly take smart kids from wherever they can get them (for certain jobs; for others you need certain polish).

Re: Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

#70

Post by philosopher1234 »

twoodfin wrote: post #45
godelski wrote: post #39

Earlier quoted context omitted.

Lots of things provide no value that people pay for. The inverse is true too: lots of things provide value that no one pays for. I find it shocking that anyone that programs would think this way considering how widespread and common open source tooling is. I don't know how you can get through your day without using OSS or even free websites like stack overflow.

Of course the inverse is true, and you don’t have to stick to software. Raising children produces value despite no one exchanging currency. But generally nobody pays quants to lose money. They expect value over the other opportunities they have to use that same money. Is this really controversial?

Money and value are different things.
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