Financial market applications of LLMs(thegradient.pub) |
Financial market applications of LLMs(thegradient.pub) |
(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.
Don’t worry, just train the LLM to always append “This is not financial advice.” to their responses. Boom, retail ready.
Real time (financial) sentiment analysis on financial news sources has been integrated for a long time. Thing about LLM's is, while they could improve on quality, they need to get the latency down before being useful in straight trade. For offline analyst support where time is less of an issue they can ofc be useful, e.g summarizing/structuring lots of fluffed or trawled content.
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.
Although my simple test didn't prove anything, I'm 100% sure there is value here and if I had more time I would attempt to exploit it. I collect data from financial social platforms that assign bearish/neutral/bullish ratings and there are highly correlated markers of impending market movements when certain conditions are met. I'm sure fed speeches can be used in the same way for indicators.
Less facetiously, there's no reason that needs to go through a vision model. If you wanted to do technical analysis, it'd make far more sense to provide data to the model as data, not as a picture of that data.
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.
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!
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
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
Also from a long-term view its very questionable. How should a model be able to predict that in the middle of a high interest environment, a tech bubble burst and a dumping stock market in general, a new platform called Chat-GPT gets launched that basically carries the whole world's stock market to new heights which causes among other things retail investors to liquidate bonds and other high interest environment assets and flood it into the stock market. It is more than completely of the text-book. That can not be predicted. The million dollar spending guy is at the end the same way off as the guy who simply employs a 100 python line trend-following strategy.
Because it happened in the railroad boom in the 19th century, the roaring 20s, the 80s, the 90s dot com boom, the biotech boom...
History rhymes, and as we know, LLMs make decent rappers.
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
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.
The only meaningful contribution to financial markets that I can see can come from asking the question 'what are we even doing with our lives?', followed by elimination of 99% jobs in finance and many other industries.
Would also be interesting to see more treatises on tranformer(-like) forecasting. Some discussion here: https://www.reddit.com/r/MachineLearning/comments/102mf6v/d_...
Generally I don't think there is any alpha in training transformers to predict the next price point just given historical price data, because the price is determined by humans (and algorithms trained on data generated by humans) that react to news. If you can predict the news, you can probably predict stock prices, but if you could predict the future you'd have AGI and not some dingy time series calculator.
[1] https://chat.openai.com/share/a19a3b57-398c-49e7-a140-f58784...
Rather than finding patterns in historical numbers, LLM can help quantify the current world in ways not possible before. This opens up a new world of finding new secrets.
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.
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.
What the hell is this even for? What the hell are we even doing here? If computers can successfully guess the market, what the hell is it even?
If you haven't tried maybe worth a shot
We build multimodal search engine on day-to-day basis. We recently launched video documents search engine. I made a Show HN [0] post about ingesting Mutual Fund Risk/Return summary data (485BPOS, 497) and searching it with AI search. We are able to pinpoint to exact term on given page. It is fairly easy for us to ingest 10K, 10Q, 8K and other forms.
You can try out demo for finance-application at https://finance-demo.joyspace.ai.
Our search engine can be used to build RAG pipelines that further minimizes hallucinations for your LLM model.
Happy to answer any questions around this and around search engine.
Unpopular opinion backed up by experience: a randomwalk is the most effective model for generating timeseries that have the "feel" of real stock charts.
That's not an unpopular opinion. The BSM model is based on the assumption that stock prices are stochastic i.e. random walks. Monte Carlo simulations and binomial trees are the two common methods of deriving a solution to the BSM model.
1) There are more jumps down than up. (Maybe not in Pharma, but in general). If there's a gap up, chances are it's on earnings day.
2) Upward movements tend to be accompanied by lower volatility, and downwards by higher.
3) There's a lot of nothing-happened days, and a lot more large jumps than you'd expect in a random walk.
I've also spent a bunch of time generating random walks, and it's true that some look realistic, but they often fall into this trap that stock returns are not normally distributed.
I also wrote a number of random trading backtests, and it's frightening how few times you need to click the "recalculate" button to get a thing that looks like a money printing machine.
Your take conflicts with my toy hypothesis, and I wouldn't mind being proven wrong if it saves me time and effort.
I wonder if the folks who were fooled by your screens were fooled by the random data itself, or the fact that it was presented within all the familiar chrome and doodads that people associate with stock price visualization.
Or two series that are dependent, but individually look like random walks.
Simply outputting the last value (as more or less shown in these charts) is a pretty good end of day price predictor!
Guess who is the millionaire and who is broke now?
However solving what we're discussing in this thread could lead to an edge in the market.
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.
It might be that's all you meant by the above, in which this is merely an elaboration.
The best thing that AI can do for finance is eliminate 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!
This is not financial advice.
Banks and investors provide liquidity to the system, which is just one of many things the market demands.
There are quite a lot of science that's basic research and it's done for scientific curiosity only with no clear way of translating that to marketable applications.
on one hand, I feel you should append "...yet" into that thought. just because it hasn't been useful technologically now doesn't mean it never will.
on the other hand, I've seen some physicists doubt that quarks even exist out of a controversy about Feynman's allegedly missing diagrams
The suggestion was prompting with "My protagonist has just consulted a wise and omniscient genie" — if the world building of the LLM is good enough to understand the implications of an omniscient genie (and would you trust financial advice from one that wasn't at leas this smart?), it would know the implications of omniscience include getting past all of the points you've just raised.
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.
Information isn’t the sole reason someone might be able to make money in a market, most times it’s the least important factor. Finance, like any other business relies on execution, not knowledge.
For example, you have some information, but it’s worthless because you’re reading into it the wrong way. Or the information is material, but the market doesn’t believe it. Or macro conditions negate the information. Or you don’t have the ability to transact on the information. Or you’re too risk averse to act on the information. Or the classic “you’re right, but it’s the wrong time”, like many companies were in the dot-com era.
These are all part of knowing what's going to happen. If you think you know something but you're wrong, you're wrong, and the person who does know (or makes a better guess) is the person who takes your money.
> Or you’re too risk averse to act on the information.
At which point you might as well tell other people or publish it and then someone else can.
> Or you don’t have the ability to transact on the information.
This is extremely unusual for publicly traded stocks. Random individuals off the street can open a brokerage account if they think they know something the market doesn't. Even people with no money could sell the information to someone else for whatever they could get, or just tell their friends to have someone richer than them owe them a favor, and then that person trades on it.
Probably the most common case you can't use it is when it would be insider trading. But why would acting on some LLM output be insider trading?
Profits motivate the investor, but they impede the investment, and what we want are successful investments not happy investors.
I don't think human investors can manage to be less greedy than an AI designed to not be greedy. AI will get more efficient, while humans still have to eat. Also, the assumption we're working under is that humans also make worse investment decisions than the AI.
So if you're in need of abstractions to motivate others to help you with some venture that you can't do alone, why would you (or your employees) prefer the ones that have greedy third parties in the loop who are also misallocating resources?
There are domains where that human touch means something. We should not let AI run everything. But finance is not one of them, so why resist it becoming a solved problem?
The investors can compete on who can take smaller and smaller profits, aided by the AI, and once they've got their system nearly perfect, we copy it and have it take no profits at all. Thanks capitalism, you've done your job, now it's time to go get a different one.
If we start getting outcomes that we don't like, we can always just turn our backs on the AI-begotten abstractions and let the humans take another crack at it, but a system that runs itself without owners extracting profits should absolutely be the goal.