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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)

#111

Post by throwaway2037 »

FabHK wrote: post #58
pinkmuffinere wrote: post #51

Earlier quoted context omitted.

Ya, I’m very surprised by the argument “you’re some of the best at numerical analysis and high frequency trading, and you’d be bad at anything else”, lol. That said, I think there are better reasons to work at such a place. Providing liquidity to the market is a good thing, and has real world value, it’s just hard for us to connect it to concrete outcomes. But as a simplified “toy” example, when they orchestrate a tr…

I think the liquidity argument is overrated. The market has two functions: 1. Incentivizing convergence of the price towards fundamental value, to support proper asset allocation decisions. 2. Supporting buying and selling (ie "liquidity") to shift consumption in time (and enable productive investments with the delayed consumption). Suppose a retirement fund holds their investments over a 20 year period on average, g…

This comment is weird to me. Are you saying that the "financial sector" (incredibly vague term) makes 30% of US GDP? Not even close. A trivial Google search proves that it is way off base.

According to this source: https://www.statista.com/statistics/248004/percentage-added-...

   > finance, insurance, real estate, rental, and leasing ... is 20.7% of US GDP.
Also, most of the GDP in the "financial sector" is in commercial banks and insurance companies. Yes, they take risk, but not the kind being discussed here.

Since the original article is about Jane Street's financial market making business, let's focus on investment banks. What percent of US GDP do you think that investment banks and trading hedge funds represent? It is tiny. I would be shocked if it is more than 5%.

    > What would happen if equity markets were only open a very short period a day? Say you have one auction a day, or maybe two, and no continuous trading?
This seems like a question from Econ 101. Let's expand that to all free markets in the US. What if homes could only be bought or sold once per month, instead of daily? How about agricultural products? Quickly this argument falls apart. Wholesale and financial markets with continuous trading have existed for centuries. The purpose of continuous trading (or very frequent auctions, like the agro auctions in the Netherlands) is price discovery. If you do it less frequently, then you have weaker price discovery and worse (less accurate) prices.

Finally, professional financial market makers have an important role to play in reducing the size of bid-ask spread. I recently bought some 1Y US Treasury bills using Interactive Brokers. I was stunned by how tight are the spreads, and I am a "Retail Normie/Nobody". Absolutely, this was not available to people like me 30 years ago. Who do you think is providing this liquidity that keeps bid-ask spreads so tight?

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

#112

Post by throw-qqqqq »

FabHK wrote: post #58
pinkmuffinere wrote: post #51

Earlier quoted context omitted.

Ya, I’m very surprised by the argument “you’re some of the best at numerical analysis and high frequency trading, and you’d be bad at anything else”, lol. That said, I think there are better reasons to work at such a place. Providing liquidity to the market is a good thing, and has real world value, it’s just hard for us to connect it to concrete outcomes. But as a simplified “toy” example, when they orchestrate a tr…

I think the liquidity argument is overrated. The market has two functions: 1. Incentivizing convergence of the price towards fundamental value, to support proper asset allocation decisions. 2. Supporting buying and selling (ie "liquidity") to shift consumption in time (and enable productive investments with the delayed consumption). Suppose a retirement fund holds their investments over a 20 year period on average, g…

> What would happen if equity markets were only open a very short period a day?

I think about it like this:

How stable are prices of low liquidity instruments compared to the most liquid instruments?

What happens in the first 10-15 mins after the markets open? EXTREME volatility.

Longer trading sessions, higher volume and more liquidity lowers volatility on average.

A hypothetical X percent worse spread on my mortgage bonds, means I have to borrow X% more to buy the house. That’s meaningful money for most people.

Market makers will still earn the spread. More trading just means it gets lowered because of competition and volume.

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

#113

Post by daymanstep »

eadmund wrote: post #108
Workaccount2 wrote: post #46

Earlier quoted context omitted.

There is an extreme case of diminishing returns at play here, and unfortunately the amount of money that flows through these markets is so incredible that it becomes worth it (more than worth it) to commission the top minds of society to push the limit as close to 1 as absolutely possible. Derivatives have great value to industry. Derivatives that require the fuel of 15 math Phds to lock in fractions of a percentage…

> Derivatives that require the fuel of 15 math Phds to lock in fractions of a percentage pricing inefficiencies so their firm can pocket the difference do not have much value at all. They have at least as much value as the total compensation of 15 math PhDs, otherwise that work would not be done.

It is bizarre that people conflate money with value.

A scammer who makes $1m dollars a year does not generate any value, in fact he removes value from society.

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

#114

Post by albumen »

lotsofpulp wrote: post #102
Workaccount2 wrote: post #73

Earlier quoted context omitted.

That is the issue. The amount of gold recovered by them as a function of the entire global gold market is a minuscule rounding error. A loss so small that when distributed across every market participant (as it would be if left alone on the ground), would amount to no practical discernible difference in anyone's life. But having 15 less top notch surgeons not doing surgery? There stands to be many practical discernib…

Gold is not analogous to trading. If you subscribe to the theory that markets allocating capital based on supply and demand is beneficial for society (even if detrimental sometimes in the short term), then traders provide the utility of contributing to the proper allocation of resources in society (which is constantly in flux). The fact that smart people opt to go into trading (or selling advertising) rather than res…

Markets influenced by traders can lead to misallocation of resources, as traders often prioritise short-term profits through speculation rather than investing in productive, long-term projects beneficial to society. Traders frequently increase market volatility, contributing little to meaningful innovation or economic growth.

Additionally, even if governments improved pay for essential roles like scientists or doctors, the outsized financial rewards from trading would still attract talent away from these critical areas. Therefore, depending on markets and government incentives alone ignores the negative impact traders’ profit-driven strategies can have on society’s overall well-being.

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

#116

Post by jakewins »

edanm wrote: post #86
__mharrison__ wrote: post #25

I read this and think: "I would love to spend a day with Jane Street and teach them how to use notebooks." So much effort is wasted because of knowledge gaps or systems that don't encourage best practices. I just taught a course to a client this week helping them with this (and other best practices for Python).

I'm intrigued - I didn't listen to the post but wonder what kind of best practices you're talking about. Do you have this written down anywhere or on video anywhere? I'd love to learn more about what you mean.

I’m intrigued as well.. My experience is notebooks struggle as a format for production code. We encourage people who work heavily in notebooks to use them for exploratory work, but choose other tools when it comes time to ship.

When you are exploring something, experimenting, showing.. it’s great; train-of-thought structure, APIs like Pandas optimised for writing and terseness etc.

But when you have a piece of code that will lose a million dollars a minute if someone ships a bug, and which will be maintained by many engineers over many years, then you really want a format that’s optimised for long-term maintenance, incremental change, testability, and APIs optimised for readers.

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

#117

Post by andrepd »

jxjnskkzxxhx wrote: post #91
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…

> 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." Complete garbage. The same way that Jane Street hires smart people that don't know anything about trading and those people contribute, the same would be true if there was money in curing cancer.

Plus, this post is about someone who quit being a doctor to work in trading!

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

#118

Post by andrepd »

pinkmuffinere wrote: post #51
Barrin92 wrote: post #24

Earlier quoted context omitted.

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…

Ya, I’m very surprised by the argument “you’re some of the best at numerical analysis and high frequency trading, and you’d be bad at anything else”, lol. That said, I think there are better reasons to work at such a place. Providing liquidity to the market is a good thing, and has real world value, it’s just hard for us to connect it to concrete outcomes. But as a simplified “toy” example, when they orchestrate a tr…

I.e., they work to increase the return on capital. Since I believe one of the biggest problems in the world today is economic inequality stemming from an increasing gulf in the remuneration of labour and the remuneration of capital, I don't think this is a good thing. Let alone a good use of stupendous amounts of talent and money.

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

#119

Post by andrepd »

eadmund wrote: post #108
Workaccount2 wrote: post #46

Earlier quoted context omitted.

There is an extreme case of diminishing returns at play here, and unfortunately the amount of money that flows through these markets is so incredible that it becomes worth it (more than worth it) to commission the top minds of society to push the limit as close to 1 as absolutely possible. Derivatives have great value to industry. Derivatives that require the fuel of 15 math Phds to lock in fractions of a percentage…

> Derivatives that require the fuel of 15 math Phds to lock in fractions of a percentage pricing inefficiencies so their firm can pocket the difference do not have much value at all. They have at least as much value as the total compensation of 15 math PhDs, otherwise that work would not be done.

It's remarkable how on every single discussion on this topic the Circular Argument is eventually brought up lol

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

#120

Post by pclmulqdq »

laidoffamazon wrote: post #66
ecshafer wrote: post #15

Earlier quoted context omitted.

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.

There are some firms that are very elitist in terms of their resume review requirements. You should note that for a place like Jane Street, "Harvard" helps, but it won't get you an interview. Someone who has done well at USAMO or IMO or has meaningful open-source contributions and goes to SUNY or a community college will generally have a better chance than a run-of-the-mill Harvard student. The median Harvard Math/CS student isn't getting an interview at Jane Street.
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