Financial Statement Analysis with Large Language Models • Hacker News Board

Re: Financial Statement Analysis with Large Language Models

#201

Post by Terr_ » Sat, May 25, 2024, 8:06 PM UTC

chronic94057 wrote: post #174

jimbokun wrote: post #61

Earlier quoted context omitted.

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

> "buy and hold the S&P 500 until you're ready to retire" That is bad advice. VGT Vanguard Technology ETF has outperformed S&P 500 over the past 20 years. All the people who say “VTSAX and chill” disappeared in the past 3-4 years because their cherished total passive index fund is no longer the best over long horizons. And no, the markets are not efficient.

> VGT Vanguard Technology ETF

Given the techie audience here, I want to caution that investing in the same industry as your job is a kind of anti-diversification.

A really severe example would be all the people who worked at Enron and invested everything in Enron stock.

Even if your employer/investments aren't quite so fraudulent, You don't want to be in a situation where you are long-term unemployed and are forced "sell low" in order to meet immediate needs. If only one or the other is hit, you can ride things out more effectively.

Re: Financial Statement Analysis with Large Language Models

#202

Post by telepathy » Sat, May 25, 2024, 8:28 PM UTC

chronic94057 wrote: post #174

jimbokun wrote: post #61

Earlier quoted context omitted.

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

> "buy and hold the S&P 500 until you're ready to retire" That is bad advice. VGT Vanguard Technology ETF has outperformed S&P 500 over the past 20 years. All the people who say “VTSAX and chill” disappeared in the past 3-4 years because their cherished total passive index fund is no longer the best over long horizons. And no, the markets are not efficient.

Need to invest in VT not VGT. Markets are efficient.

Re: Financial Statement Analysis with Large Language Models

#203

Post by surfingdino » Sat, May 25, 2024, 9:08 PM UTC

tempusalaria wrote: post #192

surfingdino wrote: post #28

Earlier quoted context omitted.

HFT guys won't touch GPT. The stakes are too high. If LLMs could give those guys an edge they'd be all over this tech.

A number of quant firms are among the largest global consumers of commercial LLMs The only area you absolutely can’t use LLMs is in sub ms latency

So where is matters the most.

Re: Financial Statement Analysis with Large Language Models

#204

Post by arathis » Sat, May 25, 2024, 9:14 PM UTC

benreesman wrote: post #153

flourpower471 wrote: post #15

Earlier quoted context omitted.

Not to mention, as somebody who works in quant trading doing ml all day on this kind of data. That ann benchmark is nowhere near state of the art. People didn't stop working on this in 1989 - they realised they can make lots of money doing it and do it privately.

I never traded consistently and successfully but I did do a startup with a seasoned quant trader with the ambition of using bigger models to generate novel alpha. We mopped the floor with the academics who publish but that is whiffle ball compared to a real prop outfit that lasts. Not having made it big myself I obviously don’t know the meta these days, but last I had any inside baseball, the non-stationarity and fri…

I think I understood 7 words you just said mister

Re: Financial Statement Analysis with Large Language Models

#205

Post by aleksiy123 » Sat, May 25, 2024, 9:48 PM UTC

arathis wrote: post #204

benreesman wrote: post #153

Earlier quoted context omitted.

I never traded consistently and successfully but I did do a startup with a seasoned quant trader with the ambition of using bigger models to generate novel alpha. We mopped the floor with the academics who publish but that is whiffle ball compared to a real prop outfit that lasts. Not having made it big myself I obviously don’t know the meta these days, but last I had any inside baseball, the non-stationarity and fri…

I think I understood 7 words you just said mister

I think what he is saying is.

1. Your automated system should be as fast as possible.

2. Stick with known, basic fundamental strategies.

3. Try new ideas around how to give those same strategies more predictive power (signal).

#1 is straight technical execution.

#3 is constantly evolving.

Is how I understood this.

And as sort of an afterthought I guess the better you are at #1 the less good you need to be at #3 and the worse you are at #1 the better you need to be at #3?

Re: Financial Statement Analysis with Large Language Models

#206

Post by immibis » Sat, May 25, 2024, 10:55 PM UTC

threeseed wrote: post #186

kortilla wrote: post #176

Earlier quoted context omitted.

You misunderstand the quote. It’s where brains go to die from a societal perspective. It might be stimulating and difficult for the individual but it’s useless to science.

Many advancements in computer science have come from the finance world. e.g. LMAX Disruptor was a pretty impressive concurrency library a decade ago: https://lmax-exchange.github.io/disruptor/

Who is using it besides LMAX?

Re: Financial Statement Analysis with Large Language Models

#207

Post by gromneer » Sat, May 25, 2024, 11:04 PM UTC

I don't have a horse in the race. I don't even know what financial statement analysis is. But it worries me that a novel reliance on these models for traditionally skilled labor jobs will turn into a dependence. These models use the built up experience of human practitioners to achieve similar results. But if a dependence grows, then there will be no more skilled human practitioners to further develop improved skills and knowledge for these jobs. Calcifying the skills in time.

Re: Financial Statement Analysis with Large Language Models

#208

Post by namaria » Sun, May 26, 2024, 7:59 AM UTC

gromneer wrote: post #207

I don't have a horse in the race. I don't even know what financial statement analysis is. But it worries me that a novel reliance on these models for traditionally skilled labor jobs will turn into a dependence. These models use the built up experience of human practitioners to achieve similar results. But if a dependence grows, then there will be no more skilled human practitioners to further develop improved skills…

That wouldn't be a problem if the models actually worked for that. We don't miss cobblers.

But these models only seem to perform these jobs on the surface. Enough that companies will try them and waste resources. This will just hurt the bottom line optimizer shops and boost the professionals doing quality work on the long run.

Re: Financial Statement Analysis with Large Language Models

#209

Post by [deleted] » Sun, May 26, 2024, 11:15 AM UTC

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…

[deleted]

Re: Financial Statement Analysis with Large Language Models

#210

Post by akrymski » Sun, May 26, 2024, 12:08 PM UTC

But GPT4 has been trained up to recent events, so you can't do rolling predictions using just historical data with it. If your LLM knows the future obviously it can predict well, even if the company name is hidden.