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Financial market applications of LLMs

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Re: Financial market applications of LLMs

#91

Post by bernardlunn »

jsemrau wrote: post #2

A lot of words for not bringing much new content to the discussion. I think the most interesting application of LLMs in Finance are (1) synthetic data models for data cleansing, (2) journal management, (3) anomaly tracking, (4) critiquing investments All of this should be done by professionals and nothing is "retail" ready.

As a human, I like anomaly tracking if I understand what you mean by that. LLMs are maybe 99% good and 1% totally wrong (hallucination). Lots of profit betting against the 1% totally wrong. Not hard to see when wrong but do need to act fast.

Re: Financial market applications of LLMs

#92

Post by PeterStuer »

jsemrau wrote: post #2

A lot of words for not bringing much new content to the discussion. I think the most interesting application of LLMs in Finance are (1) synthetic data models for data cleansing, (2) journal management, (3) anomaly tracking, (4) critiquing investments All of this should be done by professionals and nothing is "retail" ready.

(non informed, layman sideline perspective from casual reading on this subject over the years)

Real time (financial) sentiment analysis on financial news sources has been integrated for a long time. Thing about LLM's is, while they could improve on quality, they need to get the latency down before being useful in straight trade. For offline analyst support where time is less of an issue they can ofc be useful, e.g summarizing/structuring lots of fluffed or trawled content.

Re: Financial market applications of LLMs

#93

Post by lordnacho »

nostrademons wrote: post #32
btbuildem wrote: post #30

There were some developments using LLMs in the timeseries domain which caught my attention. I toyed with the Chronos forecasting toolkit [1], and the results were predictably off by wild margins [2] What really caught my eye though was the "feel" of the predicted timeseries -- this is the first time I've seen synthetic timeseries that look like the real thing. Stock charts have a certain quality to them, once you've…

I used to work in financial software, and when writing the charting UIs, I'd wire them up to a randomwalk to generate fake time series data. It was a relatively common occurrence for a VP or the company CEO to walk by, look at my screen, and say "What stock is that? Looks interesting." Unpopular opinion backed up by experience: a randomwalk is the most effective model for generating timeseries that have the "feel" of…

You can tell a stock time series by certain characteristics:

1) There are more jumps down than up. (Maybe not in Pharma, but in general). If there's a gap up, chances are it's on earnings day.

2) Upward movements tend to be accompanied by lower volatility, and downwards by higher.

3) There's a lot of nothing-happened days, and a lot more large jumps than you'd expect in a random walk.

I've also spent a bunch of time generating random walks, and it's true that some look realistic, but they often fall into this trap that stock returns are not normally distributed.

I also wrote a number of random trading backtests, and it's frightening how few times you need to click the "recalculate" button to get a thing that looks like a money printing machine.

Re: Financial market applications of LLMs

#94

Post by imtringued »

ycombobreaker wrote: post #27
ben_w wrote: post #13

Earlier quoted context omitted.

"Buy index funds. The end." From what I've heard (and as finance isn't my field, my knowledge should be considered worse than ChatGPT ), if everyone had a truly omniscient genie, the markets would become perfectly efficient, and a perfectly efficient market has no room for profit because any profit opportunity is immediately arbitraged out of existence.

A perfectly efficient market is the asymptote, you would never actually reach it. In any case, if everyone had an omniscient genie, then free will would clearly not exist the way we understand it. That doesn't sound like a fun world, regardless of financial markets!

Yeah sure but economists love it. They built entire models around this idea.

Re: Financial market applications of LLMs

#95

Post by imtringued »

whiplash451 wrote: post #89
tiborsaas wrote: post #84

Earlier quoted context omitted.

Take particle physics for example, the LHC was incredibly expensive and most people's life won't be better that that we've found the Higgs field. It was also not paid by investors but public money. There are quite a lot of science that's basic research and it's done for scientific curiosity only with no clear way of translating that to marketable applications.

That’s an extreme example. To @jonah’s point, you could also decide to work for (or build) a profitable company that solves the issues he mentioned —- without having to « embed » it into financial gaming.

Academia is primarily funded by the government through grants.

Re: Financial market applications of LLMs

#96

Post by imtringued »

multicast wrote: post #50
wuj wrote: post #39

HFTs exploit price inefficiencies that last only milliseconds. The time-series data mentioned in the article is on the scale of seconds. I wonder if its possible to get the time-series data on the scale of milliseconds, and how that would affect the training of the objective function in a LLM.

Todays derivatives and their pricing are based on the premise that stock prices can not be predicted and behave like a Brownian motion system. If you take real time data from any stock and calculate in order how many times a stock went up in a row or down in a row you end up almost perfectly with a natural probability distribution. HFT's are involved in market making and arbitrage both of which already involves high…

The tech is different but the people are the same.

Re: Financial market applications of LLMs

#97

Post by dataexp »

lordnacho wrote: post #93
nostrademons wrote: post #32

Earlier quoted context omitted.

I used to work in financial software, and when writing the charting UIs, I'd wire them up to a randomwalk to generate fake time series data. It was a relatively common occurrence for a VP or the company CEO to walk by, look at my screen, and say "What stock is that? Looks interesting." Unpopular opinion backed up by experience: a randomwalk is the most effective model for generating timeseries that have the "feel" of…

You can tell a stock time series by certain characteristics: 1) There are more jumps down than up. (Maybe not in Pharma, but in general). If there's a gap up, chances are it's on earnings day. 2) Upward movements tend to be accompanied by lower volatility, and downwards by higher. 3) There's a lot of nothing-happened days, and a lot more large jumps than you'd expect in a random walk. I've also spent a bunch of time…

Perhaps it would be easy to code a pseudo trading sequence given a model of the psychological state of the agents in the trading system

Re: Financial market applications of LLMs

#98

Post by altdataseller »

nexuist wrote: post #82
jonahx wrote: post #20

Earlier quoted context omitted.

> is that gaming financial markets is the only real application of anything scientific medicine (living longer, curing disease, vaccines, etc), cheaper energy, cheaper transportation, cheaper construction, cheaper food, better communication, new forms of entertainment, just off the top of my head.

I've sort of come around on this. Yes, everything you listed is valuable and good. But the reality is all of it was built with money that came from banks and investors. The only reason to do anything scientific is to get investors to give you money. If you do something scientific that does not make people want to give you money you will impact no lives. In this way gaming financial markets is indeed the only point to…

This sounds… kind of obvious? Very few entities are non profits or governments. Of course companies specializing in science are in for the $$$…

Re: Financial market applications of LLMs

#99

Post by profsummergig »

jsemrau wrote: post #2

A lot of words for not bringing much new content to the discussion. I think the most interesting application of LLMs in Finance are (1) synthetic data models for data cleansing, (2) journal management, (3) anomaly tracking, (4) critiquing investments All of this should be done by professionals and nothing is "retail" ready.

Could someone please clarify what "journal management" means?

Re: Financial market applications of LLMs

#100

Post by NovemberWhiskey »

jsemrau wrote: post #2

A lot of words for not bringing much new content to the discussion. I think the most interesting application of LLMs in Finance are (1) synthetic data models for data cleansing, (2) journal management, (3) anomaly tracking, (4) critiquing investments All of this should be done by professionals and nothing is "retail" ready.

The most interesting applications for LLMs in finance are basically all summarization.
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