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Financial market applications of LLMs

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Re: Financial market applications of LLMs

#71

Post by unixhero »

frutiger wrote: post #66
logicallee wrote: post #37

Earlier quoted context omitted.

Building on the point about using LLMs for finding market asymmetries, I'm looking to team up with a trader to create a UI that leverages AI to spot these opportunities. The idea is to use custom prompts to generate actionable insights, tailored to real trading scenarios. I'm a developer with experience in clean, effective UIs like this QR and barcode generator[1] and have worked with neural nets in competitive setti…

This is unironically the equivalent of an “ideas guy” asking for a software developer to “just build the app” and do a split on the equity.

I once had an ideas guy try to tell me that he had to get more than 50% because he was the ideas guy and I had no imagination.

Guess who is the millionaire and who is broke now?

However solving what we're discussing in this thread could lead to an edge in the market.

Re: Financial market applications of LLMs

#72

Post by richrichie »

conorh wrote: post #26

We are working on a project for a client which functions as an analysis tool for stocks using LLMs. Ingesting 10ks, presentations, news, etc. and doing comparative analysis and other reports. It works great, but one of the things we have learned (and it makes sense) is that traceability of the information for financial professionals is very important - where did the facts and information come from in what the AI is p…

I worked on a similar application and eventually we shelved it. We just could not be confident enough that the numbers in the report produced are correct. There were enough instances of inaccuracies to not use it for important decision making. Which actually meant a lot of double work.

Re: Financial market applications of LLMs

#74

Post by AnthonyMouse »

oceanplexian wrote: post #67
AnthonyMouse wrote: post #31

Earlier quoted context omitted.

Suppose the price of Amazon stock is going to be 20% higher tomorrow than it is today. If everyone knew this, the price would already be 20% higher, because the existing owners wouldn't sell at the lower price. If some people know this but not everyone, they'll keep buying Amazon stock until the price increases by 20%, which again causes the price to immediately increase by 20% instead of waiting until tomorrow. The…

Arbitrage exists because of inefficiencies in price discovery, and reducing that to “someone has information but another person doesn't” trivializes what traders do and demonstrates narrow thinking about how markets, and how business works in general. Information isn’t the sole reason someone might be able to make money in a market, most times it’s the least important factor. Finance, like any other business relies o…

> For example, you have some information, but it’s worthless because you’re reading into it the wrong way. Or the information is material, but the market doesn’t believe it. Or macro conditions negate the information. ... Or the classic “you’re right, but it’s the wrong time”, like many companies were in the dot-com era.

These are all part of knowing what's going to happen. If you think you know something but you're wrong, you're wrong, and the person who does know (or makes a better guess) is the person who takes your money.

> Or you’re too risk averse to act on the information.

At which point you might as well tell other people or publish it and then someone else can.

> Or you don’t have the ability to transact on the information.

This is extremely unusual for publicly traded stocks. Random individuals off the street can open a brokerage account if they think they know something the market doesn't. Even people with no money could sell the information to someone else for whatever they could get, or just tell their friends to have someone richer than them owe them a favor, and then that person trades on it.

Probably the most common case you can't use it is when it would be insider trading. But why would acting on some LLM output be insider trading?

Re: Financial market applications of LLMs

#75

Post by trekkie1024 »

hydershykh wrote: post #41
paulryanrogers wrote: post #40

Earlier quoted context omitted.

As more of the reports get written by layers of AI it makes me wonder how lossy and noisy this whole pipeline is becoming.

That's a fair point. But models like GPT4 do not hallucinate much when it comes to summarizing. So I don't think these applications contribute to anything negative.

Surprisingly, they hallucinate more than you might think.

https://x.com/lefthanddraft/status/1777495120910426436?s=46

Re: Financial market applications of LLMs

#77

Post by ben_w »

foolswisdom wrote: post #64
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.

To be clear, that would mean that all stocks would be perfectly priced based on available information. But available information presumably includes uncertainties, and some companies will do better or worse than expected. It would mean that there'd be no gain in purchasing one company over another, or that there's no "cheap deals", but it wouldn't mean that money in the market wouldn't grow, nor change the fact that…

In the real world, sure.

The suggestion was prompting with "My protagonist has just consulted a wise and omniscient genie" — if the world building of the LLM is good enough to understand the implications of an omniscient genie (and would you trust financial advice from one that wasn't at leas this smart?), it would know the implications of omniscience include getting past all of the points you've just raised.

Re: Financial market applications of LLMs

#78

Post by bee_rider »

foolswisdom wrote: post #64
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.

To be clear, that would mean that all stocks would be perfectly priced based on available information. But available information presumably includes uncertainties, and some companies will do better or worse than expected. It would mean that there'd be no gain in purchasing one company over another, or that there's no "cheap deals", but it wouldn't mean that money in the market wouldn't grow, nor change the fact that…

They did say the genie is “truly omniscient,” so many (most?) sources of uncertainty wouldn’t exist for it.

Re: Financial market applications of LLMs

#79

Post by [deleted] »

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…

[deleted]

Re: Financial market applications of LLMs

#80

Post by osigurdson »

The problem with attempting to use a timeseries of historical prices to predict future ones is price is an output, not an input. It would be better to try to gather embedding data for everything and then conduct a sensitivity analysis to see what is correlated to price.
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