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Financial Statement Analysis with Large Language Models

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Re: Financial Statement Analysis with Large Language Models

#111

Post by apwell23 »

m463 wrote: post #26
apwell23 wrote: post #20

Earlier quoted context omitted.

> citizens to ask meaningful questions about the finances of their local government. is there a demand for this. I live in cook country. I really don't want to ask these questions. Not sure what I get out of asking these questions other than anger and frustration.

if all the citizens can ask these questions, I think it will make a difference. and of course, the follow-up questions. Like who.

> if all the citizens can ask these questions, I think it will make a difference.

Our major just appointed some pastor to a high level position in CTA( local train system) as some sort of patronage.

Thats the a level things operate in our govt here. I am skeptical that some sort of data enlightenment in citenzery via llm is what is need for change.

edit: looks like the pastor buckled today https://blockclubchicago.org/2024/05/24/pastor-criticized-fo...

Re: Financial Statement Analysis with Large Language Models

#112

Post by flourpower471 »

Izkata wrote: post #89
posting_mess wrote: post #60

Earlier quoted context omitted.

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…

> 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 reasonably high level before you will be sure to see players not making obvious blunders.

Why do tech firms want developers who can write bubble sort backward in assembly when they'll never do anything that fundamental in their career? Because to get to that level you have to (usually) build solid mastery of the stuff you will use.

Trading is truly a complex endeavour - anybody who says it isn't has never tried to do it from scratch.

Id say the industry average for somebody moving to a new firm and trying to replicate what they did at their old firm is about 5%.

Im not sure what you'd call a problem where somebody has seen an existing solution, worked for years on it and in the general domain, and still would only have a 5% chance of reproducing that solution.

Re: Financial Statement Analysis with Large Language Models

#113

Post by 3abiton »

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…

Then companies tends to utilize LLMs to maximize the confusion of a message to the shareholders, cat and mouse game.

Re: Financial Statement Analysis with Large Language Models

#114

Post by refulgentis »

nebula8804 wrote: post #53
chollida1 wrote: post #19

So the history of this type of research as I know it was that we - started to diff the executives statements from one quarter to another. Like engineering projects alot of this is pretty standard so the starting point is the last doc. Diffing allowed us to see what the executives added and thought was important and also showed what they removed. This worked well and for some things still does, this is what a warrant…

I have no window into this world but I am curious if you know anything about the techniques that investors used to short or just analyze Tesla stock during the production hell of 2017-2020? It was an interesting window in ways that firms use to measure as much of the company as they can from the outside. In fact was there any other stock that was as heaving watched during that time? Looking back at that era it seemed…

Markets aren't sports teams, i.e. bimodal camps with us vs. them drama. Twitter discussion of markets, maybe, but not markets.

I've been on both sides of this trade, regularly.

Bear thesis back then was same as now. In retrospect, I give it a few more credits because Elon says they were getting close to bankrupt while he was posting "bankwupt" memes and selling short shorts.

Being a pessimist, and putting your money where your mouth is in markets, is difficult because you have to be right and have the right timing.

Re: Financial Statement Analysis with Large Language Models

#115

Post by aniviacat »

flourpower471 wrote: post #107
posting_mess wrote: post #94

Earlier quoted context omitted.

They hire people who know that maths doesn't "top out here", so they can point to them and say "look at that mathematicians/physicists/engineers/PHD's we employ - your $20Bn is safe here". Hedge funds aren't run by idiots, just a different kind of "smart" to an engineer. The engineers are are incredibly smart people, and so the bots are "incredibly smart" but "finance" is criticised by "true academics" because financ…

It is just a different (applied) discipline. It's like math v engineering - you can come up with some beautiful pde theory to describe this column in a building will bend under dynamic load and use it to figure out exactly the proportions. But engineering is about figuring out "just make its ratio of width to height greater than x" Because the goal is different - it's not about coming up with the most pleasing descri…

Claiming that being smart isn't required for trading is not the same as claiming that people doing trading aren't smart.

(Note that I personally have no opinion on this topic, as I'm not sufficiently informed to have one.)

Re: Financial Statement Analysis with Large Language Models

#117

Post by areoform »

flourpower471 wrote: post #103
posting_mess wrote: post #60

Earlier quoted context omitted.

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…

Well I work in prop trading and have only ever worked for prop firms- our firm trades it's own capital and distributes it to the owners and us under profit share agreements - so we have no incentive to sell ourselves as any smarter than the reality. Saying it's all high school math is a bit of a loaded phrase. "High school math" incorporates basically all practical computer science and machine learning and statistics…

Do you work for rentech?

Re: Financial Statement Analysis with Large Language Models

#118

Post by flourpower471 »

posting_mess wrote: post #73
qeternity wrote: post #68

Earlier quoted context omitted.

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 derive…

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 if the price is certain types of random walk and also crucially a martingale and Markov - then there is a closed form answer.

First and foremost black scholes is inconsistent with the market it tries to describe (vol smiles anyone??), so anybody claiming it's how you should price options has never been anywhere near trading options in a way that doesn't shit money away.

In reality the assumptions don't hold - log returns aren't gaussian, the process is almost certainly neither Markov or martingale.

The guys doing the very best option pricing are building empirical (so not theoretical) models that adjust for all sorts stuff like temporary correlations that appear between assets, dynamics of how different instruments move together, autocorrelation in market behaviour spikes and patterns of irregular events and hundreds of other things .

I don't know of any firm anywhere that is trading profitably at scale and is using 20 year old or even purely theoretical models.

The entire industry moved away from the theory driven approach about 20 years ago for the simple reason that is inferior in every way to the data driven approach that now dominates

Re: Financial Statement Analysis with Large Language Models

#119

Post by JumpCrisscross »

posting_mess wrote: post #73
qeternity wrote: post #68

Earlier quoted context omitted.

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 derive…

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…

> the way to calculate the price of an Option/Derivative hasn't changed in my understanding for 20/30 years

Not true. Most of the magic happens in estimating the volatility surface, BSM's magic variable. But I've also seen interesting work in expanding the rates components. All this before we get into the drift functions.

Re: Financial Statement Analysis with Large Language Models

#120

Post by chronic7202h »

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…

> Id say the industry average for somebody moving to a new firm and trying to replicate what they did at their old firm is about 5%.

Because 95% of experienced candidates in trading were fired or are trying to scam their next employer.

“Oh, yeah, my can do sharpe for million pnl per year. Trust me bro”. Fucking annoying

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