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Financial Statement Analysis with Large Language Models

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Re: Financial Statement Analysis with Large Language Models

#141

Post by lxgr »

worik wrote: post #126

I am disturbed to see so much enthusiasm here for "trading" Markets matter, and some speculation is useful, but the purpose of markets is not speculation. Obviously. If you want to make some money get trained and get a good salary. Save your money in safe assets if you want to get supper rich be super creative, ensure going broke will only effect you (i.e. do not do this while supporting a family), and found a firm.…

Don't even know where to begin with this:

No, not all of finance is a zero-sum game. If you're connecting a buyer and seller that otherwise wouldn't have met, you provided value. Same for connecting them through time (in that you can e.g. help prevent somebody having to panic-sell their house from getting a suboptimal price).

Sure, there's speculation, nepotism, corruption; there are immoral and illegal market practices with no end, but you're making it sound like that's the entire purpose of finance, and not an undesirable byproduct.

Also, as if these only exist there, and not everywhere where there is power and money: Politics, business, even charity are not immune.

Starting a company is more ethical than trading – seriously? While there might be a general trend, can you think of no philanthropic traders and of no unethical founders (some of them in jail)?

> If you want to make some money get trained and get a good salary. Save your money in safe assets

100% agreement on the first part. But if everybody invests their money in "safe assets", there is no capital for people to start companies other than banks. Is that desirable? And who even determines what a safe asset is? What about people that manage and allocate risk? That's a function of finance again!

> Friends, look after friends. Maximise happiness. Be honest, be ethical, be safe

I agree, but this arguably has little to do with the remainder of your sweeping generalization.

Re: Financial Statement Analysis with Large Language Models

#142

Post by famouswaffles »

caseyy wrote: post #87

> In this section, we aim to understand the sources of GPT’s predictive ability. Oh boy... I wonder how a neural net trained with unsupervised learning has a predictive ability. I wonder where that comes from... Unfortunately, the article doesn't seem to reach a conclusion. > We implement the CoT prompt as follows. We instruct the model to take on the role of a financial analyst whose task is to perform financial sta…

>Who will tell them how an LLM works and that the neural net does not calculate anything?

You don't understand LLMs as well as you think you do. Yes, the neural network calculates things.

>It only predicts the next token in a sentence of a calculation if it's been loss-minimized for that specific calculation.

No that's not necessary at all.

https://www.alignmentforum.org/posts/N6WM6hs7RQMKDhYjB/a-mec...

https://cprimozic.net/blog/reverse-engineering-a-small-neura...

Re: Financial Statement Analysis with Large Language Models

#143

Post by JonChesterfield »

madmask wrote: post #110
SavageBeast wrote: post #56

Earlier quoted context omitted.

Seems like the money here would be building a shiny, public facing version of the tool behind a robust paywall and build a relationship with a few Broker Dealer firms who can make this product available to the Financial Advisors in their network. If you were running this yourself with $1M input capital, that'd be $20k/year per 1M of input - so $20K is a nice number to try and beat selling a product that promulgates a…

As fas as I know the more people use the strategy the worse it performs, the market is not static, it adapts. Other people react to the buy/sell of your strategy and try to exploit the new pattern.

This is an interesting observation in combination with the popular pension strategy to continually buy index funds regardless of performance.

Re: Financial Statement Analysis with Large Language Models

#145

Post by [deleted] »

next_xibalba wrote: post #140
Izkata wrote: post #89

Earlier quoted context omitted.

> but most "trading" isn't as complex as they'd like you/us to believe I know nothing about this world, but with things like "doctor rediscovers integration" I can't help but wonder if it's not deception but ignorance - that they think it really is where math complexity tops out at.

Please cite your references, lest you run afoul of the lulgodz: https://diabetesjournals.org/care/article/17/2/152/17985/A-M...

[deleted]

Re: Financial Statement Analysis with Large Language Models

#146

Post by Izkata »

flourpower471 wrote: post #112
Izkata wrote: post #89

Earlier quoted context omitted.

> but most "trading" isn't as complex as they'd like you/us to believe I know nothing about this world, but with things like "doctor rediscovers integration" I can't help but wonder if it's not deception but ignorance - that they think it really is where math complexity tops out at.

Drs rediscover integration is about people stepping far outside their field of expertise. It is neither deception or ignorance. It's the same reason some of the best physics students get PhD studentships where they are basically doing linear regression on some data. Being very good at most disciplines is about having the fundamentals absolutely nailed. In chess for example, you will probably need to get to a reasonab…

> Drs rediscover integration is about people stepping far outside their field of expertise.

> It is neither deception or ignorance.

How is it not ignorance of math?

Re: Financial Statement Analysis with Large Language Models

#147

Post by caseyy »

famouswaffles wrote: post #142
caseyy wrote: post #87

> In this section, we aim to understand the sources of GPT’s predictive ability. Oh boy... I wonder how a neural net trained with unsupervised learning has a predictive ability. I wonder where that comes from... Unfortunately, the article doesn't seem to reach a conclusion. > We implement the CoT prompt as follows. We instruct the model to take on the role of a financial analyst whose task is to perform financial sta…

>Who will tell them how an LLM works and that the neural net does not calculate anything? You don't understand LLMs as well as you think you do. Yes, the neural network calculates things. >It only predicts the next token in a sentence of a calculation if it's been loss-minimized for that specific calculation. No that's not necessary at all. https://www.alignmentforum.org/posts/N6WM6hs7RQMKDhYjB/a-mec... https://cprim…

I have heard of generalization vs memorization, but the article you shared is very high quality. Thank you.

I do not think that SOTA LLMs demonstrate grokking for most math problems. While I am a bit surprised to read how little training is necessary to achieve grokking in a toy setting (one specific math problem), the domain of all math problems is much larger. Also, the complexity of an applied mathematics problem is much higher than a simple mod problem. That seems to be what the author of the first article you quoted thinks as well.

Our public models fail in that large domain a lot. For example, with tasks like counting elements in a set (words in a paragraph). Not to mention that they fail in complex applied mathematics tasks. If they have been loss-minimized for that specific calculation to the point that they exhibit this phase change, then that would be an exception.

But in the financial statement analysis article, the author says explicitly that there isn't a limitation on the types of math problems they ask the model to perform. This is very, very irregular, and there are no guarantees that model has generalized them. In fact, it is much more likely that it hasn't, in my opinion.

In any case, thank you again for the article. It's just such a massive contrast with the MBA article above.

Re: Financial Statement Analysis with Large Language Models

#148

Post by primitivesuave »

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…

Beautifully stated. I can only speculate, but I'd say the reason it is this way is due to the collective apathy/cynicism toward government. We have collectively come to expect a certain level of corruption and influence peddling. We have a high tolerance for incompetence in carrying out government operations. Only the most egregious offenders are brought to the public's attention, and in an age of increasingly short attention spans, people have forgotten by the time elections roll around.

That is, if they vote in the first place - in that example I gave above of a corrupt mayor stealing millions (Tiffany Henyard of Dolton, IL), the voter turnout was only 15%.

Re: Financial Statement Analysis with Large Language Models

#149

Post by TacticalCoder »

worik wrote: post #126

I am disturbed to see so much enthusiasm here for "trading" Markets matter, and some speculation is useful, but the purpose of markets is not speculation. Obviously. If you want to make some money get trained and get a good salary. Save your money in safe assets if you want to get supper rich be super creative, ensure going broke will only effect you (i.e. do not do this while supporting a family), and found a firm.…

> I am disturbed to see so much enthusiasm here for "trading"

It is lots of fun. Very mathy. A nerd's dream.

Then the data. Oh the amount of data. 34 Gbit/s to get the full US options feed last I checked (someone posted that here I think). Much of the rest is kiddie stuff compared to dealing with that.

People can lament has much as they want that it drains the great minds: it is fun.

I didn't invent that game. Don't blame the players.

Re: Financial Statement Analysis with Large Language Models

#150

Post by yumbrand »

flourpower471 wrote: post #118
posting_mess wrote: post #73

Earlier quoted context omitted.

Quantitative trading is simply the act of trading on data, fast or slowly, but I'll grant you for the more sophisticated audience there is a nuance between "HFT" and "Quant" trading. To be "super right" you just have to make money over a timeline, you set, according to your own models. If I choose a 5 year timeline for a portfolio, I just have to show my portfolio outperforming "your preferred index here" over that t…

I don't really understand your viewpoint - I assume you don't actually work in trading? Aside from the "theoretical" developments the other comment mentioned, your implication that there is some fixed truth is not reflected in my career. Anybody who has even a passing familiarity with doing quant research would understand that black scholes and it's descendants are very basic results about basic assumptions. It says…

There's no way this person works as a quant. Almost every statement they've made is wrong...
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