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?
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.
To be clear, that would mean that all stocks would be perfectly priced based on available information. But available information presumably includes uncertainties, and some companies will do better or worse than expected. It would mean that there'd be no gain in purchasing one company over another, or that there's no "cheap deals", but it wouldn't mean that money in the market wouldn't grow, nor change the fact that the S&P is likely your best option.
It might be that's all you meant by the above, in which this is merely an elaboration.
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.
Where would the profits come from? Any AI extracting them would be at a disadvantage compared to the equivalent AI that makes the same decisions otherwise but doesn't extract those profits.
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.
Not really, just do something else instead. It'll only actually happen if the AI-begotten efficiencies are real, and in that scenario there will more to go around re: supporting people whose current expertise is no longer relevant.
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!
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.
Arbitrage exists because of inefficiencies in price discovery, and reducing that to “someone has information but another person doesn't” trivializes what traders do and demonstrates narrow thinking about how markets, and how business works in general.
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.
> 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 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?
Its crazy how many people don't understand this. I can't believe how many people think they could predict the market with candle light sticks or whatever. If a method for predicting the market is so readily available that someone is selling it to you, it eouldnt work!!
(non informed, layman sideline perspective from casual reading on this subject over the years)
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.
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
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.
One other area which I think is potentially quite interesting is using LLMs to help in deciphering "Fed-speak". Eg JP Morgan built an LLM to try to predict the impact on interest rate markets of speeches by various central bank policymakers.
I conducted a test last year with GPT 4. The idea was simple. Feed Powell's official fed meeting speeches and give a rating between 1 and 10, 10 being more dovish and 1 being more hawkish. I fed around 7 or so Fed speeches and kept getting around an 8 on the rating, which would have been more dovish. There were a few speeches in there that were definitely hawkish, and the markets reacted that way as well.
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.
As a human, I like anomaly tracking if I understand what you mean by that. LLMs are maybe 99% good and 1% totally wrong (hallucination). Lots of profit betting against the 1% totally wrong. Not hard to see when wrong but do need to act fast.
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.
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.
I worked on a similar application and eventually we shelved it. We just could not be confident enough that the numbers in the report produced are correct. There were enough instances of inaccuracies to not use it for important decision making. Which actually meant a lot of double work.
If it was me, I would be ingesting the raw filings from SEC EDGAR and using the robust xml documentation to create very accurately annotated data tables that would be fed to my LLM
A coworker presented a demo the other day of this - asking LLM (I think it was OpenAI) to extract the text from a PDF - each page of the PDF passed as an image. It was able to take a table and turn it into a hierarchical representation of the data (ie. Column with bullets under it for each row, then next column, etc.)
AWS textract now has the functionality to offer a table cell based on a query - if I’m not mistaken. I’ve seen nothing similar to this and would be very interested if there are other solutions.
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.
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.
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!
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.
That’s my experience as well. A random walk looks just like market data. You could even perform technical analysis on it, finding support, resistance, trendlines, etc. It really makes you realize why technical analysis doesn’t work.
> 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.
You can tell a stock time series by certain characteristics:
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.
I have tested this with multiple veterans and none could tell them apart - but they had a high conviction on which random walks were a good buy and which were compelling shorts.
And there is your arbitrage opportunity. If you can model how analysts will react to a particular timeseries, even if it was random until that point, you have some information about the future. It'd be a good question to figure out if there is a consensus or majority about how to interpret patterns among the people making decisions or writing quant algos, that's something one could use.
That's an interesting take! You show them meaningless data, in order to extract their overall market sentiment, and use _that_ to inform your investing strategy?
This means that you don’t even need to ask the analysts to see how they react because any bias worth trading on can be predicted from the time series itself.
I'd love to see some examples, if you have old screenshots laying around!
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.
Yes, but it is also possible to generate "parameterised" random walks that have some predictability and are visually indistinguishable from "pure" random walks.
Or two series that are dependent, but individually look like random walks.
As always, when running time series predictions on financial datasets, one need to use daily return (including dividends, corporate actions, etc.) rather than end of day price.
Simply outputting the last value (as more or less shown in these charts) is a pretty good end of day price predictor!
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
That's a fair point. But models like GPT4 do not hallucinate much when it comes to summarizing. So I don't think these applications contribute to anything negative.
> 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
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.
Todays derivatives and their pricing are based on the premise that stock prices can not be predicted and behave like a Brownian motion system. If you take real time data from any stock and calculate in order how many times a stock went up in a row or down in a row you end up almost perfectly with a natural probability distribution. HFT's are involved in market making and arbitrage both of which already involves high speed, the later much more, and earning minuscule profits. There are ghost patterns who can be mined for a certain period of time but they are not solely calculated based on trading time series. They involve complex proprietary calculations, some machine learning and relationships between stocks. There is no pattern in the flow how a particular stock is trading.
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.
> 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.
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.
Derivatives are priced under those assumptions because the aim is to calculate exposure/risk (where simple / assume you're wrong is desirable), the pricing is sort of an afterthought most of the time.
> 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.
I've sort of come around on this. Yes, everything you listed is valuable and good. But the reality is all of it was built with money that came from banks and investors. The only reason to do anything scientific is to get investors to give you money. If you do something scientific that does not make people want to give you money you will impact no lives. In this way gaming financial markets is indeed the only point to doing anything ambitious at all.
Take particle physics for example, the LHC was incredibly expensive and most people's life won't be better that that we've found the Higgs field. It was also not paid by investors but public money.
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.
That’s an extreme example. To @jonah’s point, you could also decide to work for (or build) a profitable company that solves the issues he mentioned —- without having to « embed » it into financial gaming.
So far, the biggest contribution to financial markets has been hype and promises. I expect this will eventually dissipate into disappointment for most.
What would a contribution to financial markets even look like?
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.
No, not at all, given the context of stock trading. Stocks do not trade the same way today as they did in 2014. Similarly there is no point in using trading data from the 1850s given that that kind of market with those kinds of traders will never ever exist again. You can only pick a few recent months or weeks to capture current trading sentiment/technique and even then everything could get blown away after the next rate hike or international incident.
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.
Quant has been about finding secrets/patterns that no one knows. Secrets because once they are known, the benefits go away or are greatly reduced.
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.
The problem with attempting to use a timeseries of historical prices to predict future ones is price is an output, not an input. It would be better to try to gather embedding data for everything and then conduct a sensitivity analysis to see what is correlated to price.
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.
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.
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.
No philosophical discussion about what are we even doing if we’re just operating on the predictions of computers to guess equity pricing? Or operating on the predictions of the predictions of computers to guess equity pricing? This isn’t based on any real evaluation. Just pattern matching.
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?
(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.