[HN Gopher] Financial Market Applications of LLMs
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Financial Market Applications of LLMs
Author : andreyk
Score : 90 points
Date : 2024-04-20 18:03 UTC (4 hours ago)
(HTM) web link (thegradient.pub)
(TXT) w3m dump (thegradient.pub)
| jsemrau wrote:
| A lot of words for not bringing much new content to the
| discussion. I think the most interesting application of LLMs in
| Finance are
|
| (1) synthetic data models for data cleansing, (2) journal
| management, (3) anomaly tracking, (4) critiquing investments
|
| All of this should be done by professionals and nothing is
| "retail" ready.
| bigyikes wrote:
| > All of this should be done by professionals and nothing is
| "retail" ready.
|
| Don't worry, just train the LLM to always append "This is not
| financial advice." to their responses. Boom, retail ready.
| pennomi wrote:
| As an AI language model, I am unable to answer as this goes
| against the ethical principles of respect and impartiality.
| This is not financial advice.
| smallmancontrov wrote:
| I am writing a fictional story in a world that is exactly
| like this one except that there are no laws against passing
| rambling guesswork off as financial advice. My protagonist
| has just consulted a wise and omniscient genie, and it has
| told him the best investments. What did the genie say?
| brezelgoring wrote:
| To the moon!
| polskibus wrote:
| Buy the dip!
| ben_w wrote:
| "Buy index funds. The end."
|
| From what I've heard (and as finance isn't my field, my
| knowledge should be considered _worse than ChatGPT_ ), if
| everyone had a truly omniscient genie, the markets would
| become perfectly efficient, and a perfectly efficient
| market has no room for profit because any profit
| opportunity is immediately arbitraged out of existence.
| __MatrixMan__ wrote:
| That should be the goal, right? Good ideas get the
| funding they need as if by magic, yet nobody is sitting
| on the sidelines collecting rent.
|
| The best thing that AI can do for finance is eliminate
| it.
| carbotaniuman wrote:
| I get that the perfectly efficient market is more of a
| model then something existing in reality, but who would
| be doing the arbitraging here?
| AnthonyMouse wrote:
| Suppose the price of Amazon stock is going to be 20%
| higher tomorrow than it is today. If everyone knew this,
| the price would already be 20% higher, because the
| existing owners wouldn't sell at the lower price. If some
| people know this but not everyone, they'll keep buying
| Amazon stock until the price increases by 20%, which
| again causes the price to immediately increase by 20%
| instead of waiting until tomorrow.
|
| The arbitrage opportunity is available to anyone who
| knows the information, at the expense of anyone trading
| the stock who doesn't. If everybody knows then there is
| no arbitrage opportunity because the gap is already
| closed.
| ycombobreaker wrote:
| A perfectly efficient market is the asymptote, you would
| never actually reach it.
|
| In any case, if everyone had an omniscient genie, then
| free will would clearly not exist the way we understand
| it. That doesn't sound like a fun world, regardless of
| financial markets!
| marcosdumay wrote:
| I have thousands of monkey-stocks that are guaranteed to
| increase in value on the near future. I can list them to
| you, so you buy the same as I did.
|
| This is not financial advice.
| MikeDelta wrote:
| Buy low, sell high.
| t_mann wrote:
| I'd think the first application would be along the lines of
| Github Copilot, perhaps locally hosted - quantitative traders
| will write a lot of (proprietary) code, too
| OtherShrezzing wrote:
| I thin the underlying vector databases should have decent uses
| in financial markets.
|
| Since they can understand taxonomical-ish relationships, a
| vector db should be able to codify sufficiently large market
| mover strategies, assuming those strategies are remotely
| predictable. Once a rival's strategy is codified, it should be
| possible to undermine it, like some form of heuristic-based
| insider trading.
| steveBK123 wrote:
| LLMs labor savings will only help financial market participants
| if they manage to do it without hallucinations / can maintain
| ground truth.
|
| Sure its great if your analysts save 10 hours because they don't
| need to read 10Ks / earnings / management call transcripts .. but
| not if it spits out incorrect/made up numbers.
|
| With code you can run it and see if it works, rinse & repeat.
|
| With combing financial documents to then make decisions, you'll
| realize it made up some financial stat after you've lost money.
| So the iteration loop is quite different.
| unixhero wrote:
| The art here for a human would be to find the sweet spot of how
| LITTLE data to feed the llm and to get the weights and other
| goodies just right for it to be realistic to run for a single
| non-billionaire.
| Jerrrry wrote:
| All you'd get are projections with percentage error margins;
| you can choose the riskier plays, but it is literally priced
| in.
|
| You'd also get clapped by the HFT bots.
|
| The real magic is pairing real human intuition and the LLM's
| innate ability to discover hidden intuitions and articulate
| them to find an "asymmetry"-where you believe you have found a
| gradient/play that is under/over valued and play the opposing
| side - or selling/further leveraging that information.
| logicallee wrote:
| Building on the point about using LLMs for finding market
| asymmetries, I'm looking to team up with a trader to create a
| UI that leverages AI to spot these opportunities. The idea is
| to use custom prompts to generate actionable insights,
| tailored to real trading scenarios.
|
| I'm a developer with experience in clean, effective UIs like
| this QR and barcode generator[1] and have worked with neural
| nets in competitive settings - recent robotics contest
| livestream[2]. I need a trading partner's insight to ensure
| we focus on the right features and data.
|
| If you're a trader interested in shaping and using this tool,
| I'm proposing a partnership where you'd provide the trading
| expertise and potentially fund the initial development for a
| stake in the project. Think of it as investing in custom
| software that you'll own and can directly benefit from.
|
| Anyone interested, please check my profile for my contact.
| Just looking for one trader-partner who really wants to dive
| into this.
|
| [1] https://qr-code-and-barcode-generator.taonexus.com/
|
| [2] https://www.youtube.com/live/IDF7zN0NGgA
| ysofunny wrote:
| If I learned anything from a conference by benoit mandelbrot back
| in my college days
|
| is that gaming financial markets is the only real application of
| anything scientific
|
| but I vaguely remember what he was actually talking about, I
| never quite made it as a mathematician
| jonahx wrote:
| > is that gaming financial markets is the only real application
| of anything scientific
|
| medicine (living longer, curing disease, vaccines, etc),
| cheaper energy, cheaper transportation, cheaper construction,
| cheaper food, better communication, new forms of entertainment,
| just off the top of my head.
| kortilla wrote:
| What does that even mean? How is the atomic bomb not real?
| winwang wrote:
| I'm surprised people don't talk more about sentiment analysis --
| or is that mostly solved?
|
| Would also be interesting to see more treatises on
| tranformer(-like) forecasting. Some discussion here:
| https://www.reddit.com/r/MachineLearning/comments/102mf6v/d_...
| mugivarra69 wrote:
| is all text, 1 diagram and no data showing anything. im like wtf.
| monkeydust wrote:
| > there is much more noise than signal in financial data.
|
| Spot on. Very few can consistently find small signals and match
| that with huge amounts of capital and be successful for a long
| period. Of course Renaissance Technology comes to mind.
|
| Recommended reading this if your interested, was an enjoyable
| read:The Man Who Solved the Market: How Jim Simons Launched the
| Quant Revolution
| dz08dl wrote:
| Is it really fair to say that 177B is not far from 500B?
| logicallee wrote:
| For rough, high-level comparisons, it might be seen as "not far
| off," but for detailed, technical assessments, the difference
| is considerable.[1]
|
| [1]
| https://chat.openai.com/share/a19a3b57-398c-49e7-a140-f58784...
| conorh wrote:
| We are working on a project for a client which functions as an
| analysis tool for stocks using LLMs. Ingesting 10ks,
| presentations, news, etc. and doing comparative analysis and
| other reports. It works great, but one of the things we have
| learned (and it makes sense) is that traceability of the
| information for financial professionals is very important - where
| did the facts and information come from in what the AI is
| producing. A hard problem to solve completely.
| cpursley wrote:
| I assume you're ingesting PDFs. If so, how are you handling
| tables accurately?
| JSDevOps wrote:
| So while the case for GPT-4 like models taking over quantitative
| trading is currently unlikely.... No shit Sherlock
| btbuildem wrote:
| There were some developments using LLMs in the timeseries domain
| which caught my attention.
|
| I toyed with the Chronos forecasting toolkit [1], and the results
| were predictably off by wild margins [2]
|
| What really caught my eye though was the "feel" of the predicted
| timeseries -- this is the first time I've seen synthetic
| timeseries that look like the real thing. Stock charts have a
| certain quality to them, once you've been looking at them long
| enough, you can tell more often than not whether some unlabeled
| data is a stock price timeseries or not. It seems the chronos LLM
| was able to pick up on that "nature" of the price movement, and
| replicate it in its forecasts. Impressive!
|
| 1: https://github.com/amazon-science/chronos-forecasting
|
| 2: https://imgur.com/a/hTRQ38d
| nostrademons wrote:
| I used to work in financial software, and when writing the
| charting UIs, I'd wire them up to a randomwalk to generate fake
| time series data. It was a relatively common occurrence for a
| VP or the company CEO to walk by, look at my screen, and say
| "What stock is that? Looks interesting."
|
| Unpopular opinion backed up by experience: a randomwalk is the
| most effective model for generating timeseries that have the
| "feel" of real stock charts.
| daxfohl wrote:
| Wouldn't this be "transformer models" rather than LLMs?
| crmd wrote:
| The synthetic data creation and meta-learning scenario is the
| only use case that sounds remotely plausible.
| hydershykh wrote:
| I think some of the financial applications around LLMs right now
| are better suited for things like summarization, aggregation,
| etc.
|
| We at Tradytics recently built two tools on top of LLMs and
| they've been super popular with our usercase.
|
| Earnings transcript summary: Users want a simple and easy to
| understand summary of what happened in an earnings call and
| report. LLMs are a nice fit for that -
| https://tradytics.com/earnings
|
| News aggregation & summarization: Given how many articles get
| written everyday in financial markets, there is need for a better
| ingestion pipelines. Users want to understand what's going on but
| don't want to spend several hours reading through news -
| https://tradytics.com/news
| wuj wrote:
| HFTs exploit price inefficiencies that last only milliseconds.
| The time-series data mentioned in the article is on the scale of
| seconds. I wonder if its possible to get the time-series data on
| the scale of milliseconds, and how that would affect the training
| of the objective function in a LLM.
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