This would be an excellent target for poisoning.
Financial Statement Analysis with Large Language Models
161–170 of 219 posts
Re: Financial Statement Analysis with Large Language Models
#162this is a great paper
Re: Financial Statement Analysis with Large Language Models
#163great paper
Re: Financial Statement Analysis with Large Language Models
#164Earlier quoted context omitted.
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.
The average return from index funds is the benchmark that all those others are trying to beat but all the competitors trying to beat the average have a tendency to push successful strategies towards the average.
Re: Financial Statement Analysis with Large Language Models
#165Earlier 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…
> 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.
To extend the chess analogy, having the fundamentals absolutely nailed is critical at even a mid-level, because the payoff/effort ratio in avoiding blunders/mistakes is much higher than innovating or being creative.
The process of getting to a higher level involves rote learning of common tactics so you can instantly recognize opportunities, and then eventually learning deep into "opening theory" which is memorizing 10 starting moves + their replies because people much better than you have written lengthy books on the long-term ramifications of making certain moves. You're learning a vast repertoire of "existing solutions" so you can reproduce them on-demand, because those solutions are battle-tested to not have weaknesses.
Chess is a game where the amount you have to lose by being wrong is much higher than what you gain by being right. Fields where this is the case want to ensure to a greater extent that people focus on the fundamentals before they start coming up with new ideas.
Re: Financial Statement Analysis with Large Language Models
#166Earlier quoted context omitted.
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!
If we put the right checks and balances (powered by AI) in place now, we can front run the criminals, both the obvious and non-obvious crimes. We can shine light in more places and push the corruption further out of the system.
Re: Financial Statement Analysis with Large Language Models
#167Earlier 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…
> It's probably more about avoiding blunders than it is having some genius paradigm shifting idea.
I too believe this is key towards successful trading. Put in other words, even with an exceptionally successful algorithm, you still need a really good system for managing capital.
In this line of business, your capital is the raw material. You cannot operate without money. A highly leveraged setup can get completely wiped out during massive swings - triggering margin calls and automatic liquidation of positions at the worst possible price (maximizing your loss). Just ask ex-billionaire investor/trader Bill Hwang[1].
1. https://www.bloomberg.com/news/features/2021-04-08/how-bill-...
Re: Financial Statement Analysis with Large Language Models
#168Earlier quoted context omitted.
Lol try it and get back to us.
Well, see, I don't actually have a method for that. But if I did, I think my capital is low enough that I'd have more success selling it to other people than trying to exploit it myself, since the benefit would be pretty minimal if I did it with just my own savings, but could be pretty dramatic for, say, banks.
Strats tend to have limits. What works for you may fall apart with large amounts of capital. Don't discount compound interest. $10,000 compounding 30% over 20 years is 2 million without any additional capital.
Re: Financial Statement Analysis with Large Language Models
#169Earlier quoted context omitted.
> 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
Obviously not true. The deals for most of these set ups are team founders/pms are paid mostly by profit share. So the only scam is scamming yourself into a low salary position for a couple years till they fire you. Orders of magnitude more leave their jobs of their choosing than are fired.
> The deals for most of these set ups are team founders/pms are paid mostly by profit share.
These PMs are not the ones job hopping every year.
And 95% of interview candidates are not PMs.
> So the only scam is scamming yourself into a low salary position for a couple years till they fire you.
200k-300k USD salary is not low.
And 1 year garden leave / non compete? That’s literally 0.5M over 2 years for doing jack shit.
This is very appealing for tech SWEs or MBA product managers who are all talk and no walk.
But even with profit share / pnl cut, many firms pay you a salary, even before you turn a profit. It eventually gets deducted when you turn a profit.
> Orders of magnitude more leave their jobs of their choosing than are fired.
Hedge fund, maybe. Prop trading, no.
Re: Financial Statement Analysis with Large Language Models
#170Earlier quoted context omitted.
Speaking for myself and likely others with similar motivations, yes we can "figure it out" and publish something to show our work and expand the field of endeavor with our findings - OR - we can figure something profitable out on our own and use our own funds to trade our strategies with our own accounts. Anyone who has figured out something relatively profitable isn't telling anyone how they did it.
Why not then publish the strategies once outmoded, or are they in fact published? Can I go see somewhere what strategies big funds used in the 90s to make bank, which presumably no longer offer a competitive advantage? The way I can go see what computer exploits/hacks used to work when they were still secret? Maybe it's just what I know, but I can't help but think the "strategies" are a lot like security exploits--so…
Why not? Because you won't know what of your strategies is outmoded by something new because that group is not publishing their strategy, which is like yours but on steroids, either.
And then everything regresses to the Dark Forest game theory.