Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Financial Statement Analysis with Large Language Models

papers.ssrn.com

Post reply on HN
Print view View original

61–70 of 219 posts

Re: Financial Statement Analysis with Large Language Models

#61

Post by jimbokun »

yaj54 wrote: post #21
foota wrote: post #12

Earlier quoted context omitted.

I agree this isn't earth shattering, but I think the benefit here is that it's a general solution instead of one trained on financial statements specifically.

agreed. most people can't create a custom tailored finance statement model. but many people can write the following sentence: "analyze this financial statement and suggest a market strategy." and if that sentence performs as well as an (albeit old) custom model, and is likely to have compound improvements in its performance over time with no changes to the instruction sentence...

"buy and hold the S&P 500 until you're ready to retire"

Re: Financial Statement Analysis with Large Language Models

#62

Post by SavageBeast »

jimbokun wrote: post #59
BobaFloutist wrote: post #50

Earlier quoted context omitted.

I think I could probably make more money selling a tool or strategy that consistently, reliably makes ~2% more than government bonds than I could make off it myself, with my current capital.

Or just sell it to exactly one buyer with a lot of capital to invest.

That hypothetical person or organization already has an advisor in charge of their money at the smaller end or an entire private RIA on the Family Office side of things. This approach is a fools errand.

Re: Financial Statement Analysis with Large Language Models

#63

Post by airstrike »

abdullahkhalids wrote: post #30
primitivesuave wrote: post #13

The area where I see this making the most transformational change is by enabling average citizens to ask meaningful questions about the finances of their local government. In Cook County, Illinois, there are hundreds of local municipalities and elected authorities, all of which are producing monthly financial statements. There is not enough citizen oversight and rarely any media attention except in the most egregious…

The citizens ask LLMs (or more advanced future AIs) to identify if government finances are being used efficiently, and if there is evidence of corruption. The corrupt government officers then start using the AIs to try to cover up the evidence of their crimes in the financial statements. The AI possibly putting the skills of high-end and expensive human accountants (or better) into the hands of local governments. Who…

> The corrupt government officers then start using the AIs to try to cover up the evidence of their crimes in the financial statements.

There's a difference between an AI being able to answer questions and it helping cover up evidence, unless you mean "using the AIs for advice on how to cover up evidence"

Re: Financial Statement Analysis with Large Language Models

#64

Post by beepbooptheory »

hnthrowaway6543 wrote: post #34
primitivesuave wrote: post #13

The area where I see this making the most transformational change is by enabling average citizens to ask meaningful questions about the finances of their local government. In Cook County, Illinois, there are hundreds of local municipalities and elected authorities, all of which are producing monthly financial statements. There is not enough citizen oversight and rarely any media attention except in the most egregious…

Let's say LLMs work exactly as advertised in this case: you go into the LLM, say "find corruption in these financial reports", and it comes back with some info about the mayor spending millions on overpriced contracts with a company run by his brother. What then? You can post on Twitter, but unless you already have a following it's shouting into the void. You can go to your local newspapers, they'll probably ignore y…

Are people supposed to be outraged that that is too little or too much money?

That's still cheaper than sending them to prison!

Re: Financial Statement Analysis with Large Language Models

#65

Post by renewiltord »

BobaFloutist wrote: post #50
andsoitis wrote: post #44

Earlier quoted context omitted.

> Anyone who has figured out something relatively profitable isn't telling anyone how they did it. Corollary: someone who is selling you tools or strategies on how to make tons and tons of money, is probably not making tons and tons of money employing said tools and strategies, but instead making their money by having you buy their advice.

I think I could probably make more money selling a tool or strategy that consistently, reliably makes ~2% more than government bonds than I could make off it myself, with my current capital.

You can't do it because there are lots of fraudulent operators in the space. Think about it: someone comes up to you offering a way to give you risk-free return. All your ponzi flags go up. It's a market for lemons. If you had this, the only way to make it is to raise money some other way then redirect it (illegal but you'll get away with it most likely), or to slowly work your way up the ranks proving yourself till you get to a PM and then have him work your strat for you.

The fact that you can't reveal how means you can't prove you're not Ponzi. If you reveal how, they don't need you.

Re: Financial Statement Analysis with Large Language Models

#66

Post by ketzo »

abdullahkhalids wrote: post #30
primitivesuave wrote: post #13

The area where I see this making the most transformational change is by enabling average citizens to ask meaningful questions about the finances of their local government. In Cook County, Illinois, there are hundreds of local municipalities and elected authorities, all of which are producing monthly financial statements. There is not enough citizen oversight and rarely any media attention except in the most egregious…

The citizens ask LLMs (or more advanced future AIs) to identify if government finances are being used efficiently, and if there is evidence of corruption. The corrupt government officers then start using the AIs to try to cover up the evidence of their crimes in the financial statements. The AI possibly putting the skills of high-end and expensive human accountants (or better) into the hands of local governments. Who…

Corrupt government officers are one thing. But there is a ton of completely well-meaning bureaucracy in the U.S. (and everywhere!) that could benefit from a huge, huge step change in "ability to comprehend".

Bad actors will always exist but I think there's a LOT of genuine good to be done here!

Re: Financial Statement Analysis with Large Language Models

#67

Post by tempodox »

antimatter15 wrote: post #9

Figure 3 on p.40 of the paper seems to show that their LLM based model does not statistically significantly outperform a 3 layer neural network using 59 variables from 1989. This figure compares the prediction performance of GPT and quantitative models based on machine learning. Stepwise Logistic follows Ou and Penman (1989)’s structure with their 59 financial predictors. ANN is a three-layer artificial neural networ…

But I bet it uses way more energy.

Re: Financial Statement Analysis with Large Language Models

#68

Post by qeternity »

posting_mess wrote: post #60
bethekind wrote: post #24

Earlier quoted context omitted.

Do you use llama 3 for your work?

No hedge fund registered before the last 2 weeks will use Llama3 for their "prod work" beyond "experiments". Quant trading is about "going fast" or "being super right", so either you'd need to be sitting on some huge llama.cpp/transformer improvement (possible but unlikely) or its more likely just some boring math applied faster than others. Even if they are using a "LLM", they wont tell you or even hint at it - "eff…

It’s impressive how incorrect so much of this information is. High frequency trading is about going fast. There is a huge mid and low freq quant industry. Also most quant strategies are absolutely not about being “super right”…that would be the province of concentrated discretionary strategies. Quant is almost always about being slightly more right than wrong but at large scale.

What algos are you referring to derived 30 or 40 years ago? Do you understand the decay for a typical strategy? None of this makes any sense.

Re: Financial Statement Analysis with Large Language Models

#69

Post by dangerwill »

I guess this makes sense. Because while there should be some noise from the text translation into the internal representation of the financial data once ingested into the model, the authors purposefully re-formatted all the reports to be formatted consistently. That then should allow the model to essentially do less of the LLM magic and more plain linear regression of the financial stats. And often past performance does have an impact on future performance, up to a point.

I wonder what the results would have been with still-anonymized but non-fully standardized statements.

Still though, impressive.

Re: Financial Statement Analysis with Large Language Models

#70

Post by Terr_ »

ecjhdnc2025 wrote: post #48
foota wrote: post #37

Earlier quoted context omitted.

We come up with all sorts of things that are initially a step backwards, but that lead to eventual improvement. The first cars were slower than horses. That's not to suggest that Renaissance is going to start using Chat GPT tomorrow, but maybe in a few years they'll be using fine tuned versions of LLMs in addition to whatever they're doing today. Even if it's not going to compete with the state of the art models for…

Far too much in the way of "maybe in a few years" LLM prediction relies on the unspoken assumption that there will not be any gains in the state of the art in the existing, non-LLM tools. "In a few years" you'd have the benefit of the current, bespoke tools, plus all the work you've put into improving them in the meantime. And the LLM would still be behind, unless you believe that at some point in the future, a radic…

> That is, the bet is that at some point, magic emerges from the machine that renders all domain-specialist tooling irrelevant, and one or two general AI companies

I have a slightly more cynical take: Those LLMs are not actually general models, but niche specialists on correlated text-fragments.

This means human exuberance is riding on the (questionable) idea that a really good text-correlation specialist can effectively impersonate a general AI.

Even worse: Some people assume an exceptional text-specialist model will effectively meta-impersonate a generalist model impersonating a different kind of specialist!

Post reply on HN