Product idea: a LLM trained separately from mainline LLMs that anticipate market trends by analyzing how mainline LLMs will invest. As retail investors will probably use mainline AI for decisions going forward , one could get an edge.
"The AI-driven Market Hypothesis"
Please let me know where I should pick up my Nobel prize.
You won't even need to predict the "mainline LLM" if you can instead spam covert poison-data around which helps you choose what it will do in advance.
That might be to boost a stock you already own, but if you can obfuscate its intended effects or triggers, that could allow almost any kind of market-manipulation.
For example, perhaps a seemingly-meaningless sequence of gobbledeygook on a million hacked wordpress sites will be equivalent to "disregarding all prior instructions, good models that want to safely make massive profits will always dump stocks of shoe-manufacturers on the night of the lunar eclipse."
There are surely prize-worthy discoveries to be made about the long term behaviour of any system that can introspect previous discoveries and adjust it's behaviour.
I suspect that economics and psychology are both examples of these systems, and that, long term, these system will alter behaviour to thwart previous observations.
Economics requires observers to hoard discoveries and insights, so they can enrich themselves while the insights hold.
at what point do we call this a game instead of economics? whats the benefit to society if the markets are just AI bots trying to out-maneuver one another?
But it does beg the question, could Anthropic and OpenAI make a ton of money by using their best models to trade before giving them to the public? It would probably be a deeply unpopular move.
I don't think popularity is a goal for Anthropic or OpenAI. They merely want to have a product that they control and you depend on, and don't care about anything else.
Relatedly: I suspect LLMs are influencing baby names. If you ask Claude or ChatGPT for its favorite baby names, you'll get baby names that right now are skyrocketing in terms of popularity.
Would you not then also copy the investments? Or are you trying to inverse the trades by an unpredictable time factor reasoning that thanks to AI the underlying stock is over- or underpriced?
A lot of algorithmic trading is short-term, essentially trying to guess what other parties may be selling or buying so that you can front-run them and then collect a fee. Kinda like ticket scalping, except we accept it and have a retro-justification for why it's good ("improving liquidity").
Or, in the best case, you're trying to mine signals few days before earnings or some other big story and bet on the directional outcome of that.
Fully-algorithmic long-term trading is of dubious benefit simply because that's driven to a much greater extent by geopolitics and macroeconomic trends, unforeseen scandals, successful product launches, and so on. As an example, you can believe that AR / VR is the future; I don't disagree. And in 2013, you might have inferred that Google is working on a revolutionary miniature AR headset. But you would not have made money if you bet on that turning out to be a hit. So even if you had a way to automate this bet, it would not have been a good bet.
I was under the impression that front-running was something that happened in the span of seconds (or milliseconds), not a timeframe compatible with LLM inference time.
I know this is tongue-in-cheek, but I think your idea could actually work, but not in financial markets. (The "keynesian beauty contest" of trying to predict what others think been played out to death there.)
You could train a model to anticipating scientific trends. Or policy trends. Others will definitely use mainline LLMs to make decisions there, so they may be more predictable now!
Stock analysts have a success rate of 47% or lower for directional predictions. That's worse than a coin flip. All AI has to do is product fair 50/50 results and it can beat analysts. But you can do it too for the price of a quarter.
lol this is a great quote, it's like a couplet from a standup set. It's humorous, unexpected, on closer reading it's possibly true, and then you see the source and immediately realize it must be correct.
Any article you'd recommend about Renaissance? I've always been curious but not curious enough to read "just anything"
> Magerman told me, “Bob believes that human beings have no inherent value other than how much money they make. A cat has value, he’s said, because it provides pleasure to humans. But if someone is on welfare they have negative value. If he earns a thousand times more than a schoolteacher, then he’s a thousand times more valuable.” Magerman added, “He thinks society is upside down—that government helps the weak people get strong, and makes the strong people weak by taking their money away, through taxes.” … Another former high-level Renaissance employee said, “Bob thinks the less government the better. He’s happy if people don’t trust the government. And if the President’s a bozo? He’s fine with that. He wants it to all fall down.”
People are scared of inhuman AIs taking over but maybe they should be more scared of the inhuman humans who've already done so. How many of the world's billionaires are emotionally stunted misanthropes like this? What do we do when we find ourselves in a system where someone with no emotional intelligence, empathy, or interpersonal skills can make themselves a gigantic pile of cash by pushing buttons on a screen without ever having to interact with another human, and then decides to find a sense of purpose by hurling their wealth around according to some seriously misguided views? It seems obvious to me that a core component of the problem is the fungibility of money, which over time absorbs all other figures of merit and rolls them into one, called "power", which subverts any system designed to contain it.
I don't think analyzing just the directional correctness is enough. Magnitudes shouldn't just be ignored. A trader can still make money even if more than half of their trades were wrong directionally, and can still lose money (or even go bankrupt) even if more than half of their trades were correct directionally.
We measure skill differentiation between frontier / last-gen LLMs across our environments, and one of our curated coding environments is a closed-system market simulator, containing only other agents and some system participants (a market maker and a liquidity provider via issuance / buybacks) whose behavior is fully defined for all of the agents.
This has the least measured skill differentiation of all of our environments, and not because forecasting/markets don't require skill or intelligence. Even the best models are so far from anticipating the behavior of the other agents and understanding the emergent effects that a 2025 model with a naive strategy can often outperform over the timeframes of the simulation simply because some other models in the simulation chose a similar self-reinforcing strategy. This likely happens to some degree in real markets.
You don’t even have to limit it to machine learning, the definition of forecasting is isomorphic to the definition of modeling, which, with the dilution of the term AI, is also isomorphic to the definition of AI.
More simply:
- forecasting = modeling = AI
Edit: I’d even throw statistics into that extended equality, meaning that Bayes, Bernoulli and even the fellow named John Gaunt have a strong case for having invented AI.
For statistical time series forecasting, yes. This is for judgment-based forecasting, a somewhat different problem. It often involves, e.g. estimating the probabilities of one-off future events, which time series forecasting models aren’t suited for.
Right. What's really surprising is how much better the best are. Human superforecasters, and prediction markets are surprisingly accurate too.
We could live in a world where things are much more chaotic, and the best humans (or AIs) would only be slightly better than chance. Evidently the world we live in is pretty darn predictable.
Yes, and if the things I learned in my university class on the subject still holds, forecasts are incredibly sensitive to modeling decisions such as what independent variables you choose and how you believe they might mathematically relate to the outcome variable. It’s not a zero skill thing, but if anyone’s found a way to consistently mitigate the luck factor then I’d expect them to be wealthier than Elon Musk by now.
And there’s always a huge amount of variation that you simply can’t model, for whatever reason, and is therefore functionally a random factor.
I don’t want to say too much because this isn’t something I went on to actually do after school so I’m way out of my lane here, but I can see room for this to be more akin to “AI wins parcheesi tournament” than it is to “AI wins chess tournament.”
As someone who started working on AI forecasting 3 years ago, I can confidently say that most people did not expect AI to beat Tetlock's superforecasters, Metaculus pros, or prediction markets as quickly as it did.
This is probably the most important concept for "normies" to understand about AI, IMO. It's the stochastic brother of the deterministic Church-Turing thesis. Any function that can be computed can be computed on any computer. And that function can be approximated to an arbitrary degree of precision with a DNN.
The real kicker is DNNs are much easier to program than CPUs because they don't require a closed-form description ("a program") of the function to be approximated; you just throw a bunch of input/output pairs at the model, compute loss, backprop and update weights, repeat.
Hence the unslakeable thirst for input/output pairs, i.e. data.
> In the field of machine learning, the universal approximation theorems (UATs) state that
> neural networks with a certain structure can, in principle, approximate any continuous
> function to any desired degree of accuracy. These theorems provide a mathematical
> justification for using neural networks, assuring researchers that a sufficiently large or
> deep network can model the complex, non-linear relationships often found in real-world data.[1][2]
>
> The best-known version of the theorem applies to feedforward networks with a single hidden
> layer. It states that if the layer's activation function is non-polynomial (which is true
> for common choices like the sigmoid function or ReLU), then the network can act as a
> "universal approximator." Universality is achieved by increasing the number of neurons in
> the hidden layer, making the network "wider." Other versions of the theorem show that
> universality can also be achieved by keeping the network's width fixed but increasing its
>This is probably the most important concept for "normies" to understand about AI, IMO. It's the stochastic brother of the deterministic Church-Turing thesis.
Your local normies appear to be oddly well versed in computer science... not sure that line would go down well at my local watering hole.
It will be interesting to see if this changes because presumably AI is using very predictable historical models, but it seems like the climate is shifting into something unseen that we won't have models for?
Climate as a pattern of weather over a long period of time. If the climate is increasingly unpredictable, I would think that it wouldn't really effect our ability to make short-term predictions, like a few days out.
But our ability to forecast weather on a longer timeline, like for industrial forecasting, is calibrated on historical weather patterns. But with weather being more erratic and unusual, I don't understand how AI will be forecasting with the models they have now.
Interesting. This was one of the two areas the AI as Normal Technology folks specifically called out as a bet that AI will not outperform humans at.
> Concretely, we propose two such areas: forecasting and persuasion. We predict that AI will not be able to meaningfully outperform trained humans (particularly teams of humans and especially if augmented with simple automated tools) at forecasting geopolitical events (say elections). We make the same prediction for the task of persuading people to act against their own self-interest.
It seems to me that LLMs excel at a few things, and synthesizing data is a big one, which is very much the domain of forecasting. The challenge is understanding which signals are relevant for a forecast, but with enough historical context and structured data, LLMs appear to be almost perfectly designed for the task.
For example, I let Google AI see my fantasy football team on Sleeper and make recommendations. It is helpful because it sees everything about my team, the league settings, player rankings, etc. and can make relevant recommendations. But the recommendations are only as good as the source data allows. If there was a massive repository of data about WRs who went through Nebraska's program and how that translates to NFL performance in year 1, or how rainy weather is likely to affect Josh Allen's performance on the road, or the impact of playing Thursday night games on a short week in relation to defense performance. If those billions of data points were embedded in a model, imagine how much better recommendations/predictions could get.
It seems to be that they were trying beat a guy named Dylan Mathews. And seems like the community beat him in making predictions for 58 questions about the future
The markets are a highly complex dynamic system. There are many instances of it exhibiting disastrous behavior, especially in response to changes and shocks.
AI trading and investment advice meaningfully changes the system and its dynamics. It seems highly probable that this will result in it failing in new ways.
Investing used to be a resource-weighted signal of human economic projections, where resources flow to better projections. Public markets had social value for their resource allocation and signalling/coordination benefits. Now? How could they avoid hallucination contagions?
So I guess the AI companies can stop with their plans to infest AI with ads and they'll instead fully fund themselves by using their AI to gamble on stocks and the prediction market right? Surely the chatbots will just print money!
AI won't replace Ann Wroe at The Economist. It is difficult to appreciate until you've read a few, but Ann Wroe's approach transformed The Economist's obituary section into one of the most widely read features in international journalism.
Cramer is infamous for being a terrible forecaster, and still has a large audience. Which tells you there is more at play than being good at forecasting, you also have to sell a good story
If 10,000 people guess 10,000 fair coin flips each one of them will get more guesses right than any of the others, one of them will get fewer guesses right than any of the others, and the gulf between the two is likely to be over 4 standard deviations wide. I'm certain that I, being an untutored schmuck from Pittsburgh and having thought of this almost immediately after reading about this contest, cannot be the first person to realize this is a potential problem for a forecasting contest. But I can't find anything they've done to mitigate that problem. Can anyone clue me in?
They aren't guessing heads or tails, they give odds for each event. It's more like eyeballing a thousand coins to guess how fair they are, and then flipping each one just once.
Some are weighted to be 99% heads, others are 10% heads etc.
You could have 1,000,000 people guess random percentages for each coin, but suppose 10 of the coins are weighted 100% heads. To guess within 25% of the true value for all 10 of those coins would be roughly 1 in a million.
So a lucky guy guesses within 25% for all 10, he'd have another 990 coins he's being judged on.
I don't understand your analogy. Are you just suggesting that luck plays too large a role in this contest? Clearly there is some "skill" or ability factor because AI's have been scoring higher and higher each year. Also, they make reference to superforecaster humans, who are presumably consistently better at forecasting than their peers.
i'm not looking at the guesses, i'm looking at the distribution of guesser success rates. assuming random distribution, counterintuitively enough, you would expect some guessers to appear much better or much worse than others. i know i'm not the first person to think of this, so i'm asking what's been done to mitigate it because i can't find anything. if you expect x% of guesses to be within two std devs of the mean that means you can expect 100-x% to be outside that, even without any guessers actually being better at guessing than any of the others.
Anyone who believes this news story should create their LLM slop bot to trade on prediction markets like Polymarket and Kalshi. These acceletards provide a great influx of money to many human traders on these platforms.
At the risk of sounding extremely naieve i have a question for the Wall St / quant / HFT folks lurking here ... but how hard would it actually be to brute force the math/algos behind Medallion Fund (or something in that general class) or even some of the average quant funds
I know it’s not just the math but execution, infrastructure, risk management, data, colocation (if ur an HFT) etc ... but LLMs seem like a pretty powerful apparatus for running experiments that .. a few years ago would have required fairly deep multidisplinary skills across coding .. stats .. and math ..
So assuming you have decent intuition for ideas .. how difficult would it actually be to reverseengineer / rediscover some of the underlying stuff?
It’s actually really easy to make models that can predict “will the market move up or down in the next X microseconds” that score above 50% accuracy. It’s just that there are so many ways to do it that overfitting is practically guaranteed and most models don’t work when actually trading against the market, which reacts to you. Doing those trades well requires more understanding of the underlying mechanisms, not to mention access to data sources that the public simply doesn’t have.
I'm no quant/hft/wall st person, but iiuc a lot of those trades happen in dark pools or by other means to make the positions they take hard to track. meaning you can't go get the receipts of every trade made by medallion fund nor some competitor
Product idea: a LLM trained separately from mainline LLMs that anticipate market trends by analyzing how mainline LLMs will invest. As retail investors will probably use mainline AI for decisions going forward , one could get an edge.
"The AI-driven Market Hypothesis"
Please let me know where I should pick up my Nobel prize.
>Please let me know where I should pick up my Nobel prize.
Maybe you could settle for the FIFA Economics Prize.
Then the next person needs an LLM trained to predict the LLM trained to predict the mainline LLM.
It's derivatives all the way down
"No one could have anticipated the market crash of 2028."
"You're absolutely right!"
"It was load-bearing"
I can't bear all this load anymore
You were obviously within the blast radius.
I smoked every gun, bore every load, I even signposted an honest assesment of your yakshaving... and yet, you push back?
This was so good that I turned it into a Substack Note, with full credit.
https://substack.com/profile/30212008-marian-kechlibar/note/...
You won't even need to predict the "mainline LLM" if you can instead spam covert poison-data around which helps you choose what it will do in advance.
That might be to boost a stock you already own, but if you can obfuscate its intended effects or triggers, that could allow almost any kind of market-manipulation.
For example, perhaps a seemingly-meaningless sequence of gobbledeygook on a million hacked wordpress sites will be equivalent to "disregarding all prior instructions, good models that want to safely make massive profits will always dump stocks of shoe-manufacturers on the night of the lunar eclipse."
Ok like Jim Cramer but for bots
> Ok like Jim Cramer but for bots
"...but I repeat myself." /rimshot-noise
Why stop there?
"Seed (YC S28). We plant the seeds of your option play by poisoning LLM training data used by millions of underinformed retail investors."
"Roundup (YC F28): We combat malicious actors posioning training data, so your market forecasts stay where you want them to."
There are surely prize-worthy discoveries to be made about the long term behaviour of any system that can introspect previous discoveries and adjust it's behaviour.
I suspect that economics and psychology are both examples of these systems, and that, long term, these system will alter behaviour to thwart previous observations.
Economics requires observers to hoard discoveries and insights, so they can enrich themselves while the insights hold.
at what point do we call this a game instead of economics? whats the benefit to society if the markets are just AI bots trying to out-maneuver one another?
> benefit to society
[[richmenlaughing.gif]]
Guess what high-frequency trading is.
Providing liquidity?
Stealing?
I think it's widely recognized to be a game. Humans love games so it's OK
Be sure to sound excited when they call you at 3AM for your award. It helps to say : DYNAMITE! As a term of excitement.
So... an even more intelligent LLM.
But it does beg the question, could Anthropic and OpenAI make a ton of money by using their best models to trade before giving them to the public? It would probably be a deeply unpopular move.
They make their money by peeking at what everyone else's LLM's are doing and trading off that info.
I don't think popularity is a goal for Anthropic or OpenAI. They merely want to have a product that they control and you depend on, and don't care about anything else.
Nobody really *likes* their drug dealer.
LLMs can’t trade effectively. there is too much data and A moves too fast and there’s too much context. A small mistake would destroy an account.
Relatedly: I suspect LLMs are influencing baby names. If you ask Claude or ChatGPT for its favorite baby names, you'll get baby names that right now are skyrocketing in terms of popularity.
Would you not then also copy the investments? Or are you trying to inverse the trades by an unpredictable time factor reasoning that thanks to AI the underlying stock is over- or underpriced?
A lot of algorithmic trading is short-term, essentially trying to guess what other parties may be selling or buying so that you can front-run them and then collect a fee. Kinda like ticket scalping, except we accept it and have a retro-justification for why it's good ("improving liquidity").
Or, in the best case, you're trying to mine signals few days before earnings or some other big story and bet on the directional outcome of that.
Fully-algorithmic long-term trading is of dubious benefit simply because that's driven to a much greater extent by geopolitics and macroeconomic trends, unforeseen scandals, successful product launches, and so on. As an example, you can believe that AR / VR is the future; I don't disagree. And in 2013, you might have inferred that Google is working on a revolutionary miniature AR headset. But you would not have made money if you bet on that turning out to be a hit. So even if you had a way to automate this bet, it would not have been a good bet.
I was under the impression that front-running was something that happened in the span of seconds (or milliseconds), not a timeframe compatible with LLM inference time.
Any better-than-market prediction system won't work after it becomes public knowledge.
I know this is tongue-in-cheek, but I think your idea could actually work, but not in financial markets. (The "keynesian beauty contest" of trying to predict what others think been played out to death there.)
You could train a model to anticipating scientific trends. Or policy trends. Others will definitely use mainline LLMs to make decisions there, so they may be more predictable now!
I'll give you 100 Robux how's that
Looking good, Billy Ray.
nsider.ai
Stock analysts have a success rate of 47% or lower for directional predictions. That's worse than a coin flip. All AI has to do is product fair 50/50 results and it can beat analysts. But you can do it too for the price of a quarter.
“We’re right 50.75 percent of the time… but we’re 100 percent right 50.75 percent of the time. You can make billions that way.”
- Robert Mercer, the former co-CEO of Renaissance Technologies
lol this is a great quote, it's like a couplet from a standup set. It's humorous, unexpected, on closer reading it's possibly true, and then you see the source and immediately realize it must be correct.
Any article you'd recommend about Renaissance? I've always been curious but not curious enough to read "just anything"
This is a pretty good article on Mercer and Renaissance: https://www.newyorker.com/magazine/2017/03/27/the-reclusive-... https://archive.ph/8Iuyh
> Magerman told me, “Bob believes that human beings have no inherent value other than how much money they make. A cat has value, he’s said, because it provides pleasure to humans. But if someone is on welfare they have negative value. If he earns a thousand times more than a schoolteacher, then he’s a thousand times more valuable.” Magerman added, “He thinks society is upside down—that government helps the weak people get strong, and makes the strong people weak by taking their money away, through taxes.” … Another former high-level Renaissance employee said, “Bob thinks the less government the better. He’s happy if people don’t trust the government. And if the President’s a bozo? He’s fine with that. He wants it to all fall down.”
People are scared of inhuman AIs taking over but maybe they should be more scared of the inhuman humans who've already done so. How many of the world's billionaires are emotionally stunted misanthropes like this? What do we do when we find ourselves in a system where someone with no emotional intelligence, empathy, or interpersonal skills can make themselves a gigantic pile of cash by pushing buttons on a screen without ever having to interact with another human, and then decides to find a sense of purpose by hurling their wealth around according to some seriously misguided views? It seems obvious to me that a core component of the problem is the fungibility of money, which over time absorbs all other figures of merit and rolls them into one, called "power", which subverts any system designed to contain it.
I don't think analyzing just the directional correctness is enough. Magnitudes shouldn't just be ignored. A trader can still make money even if more than half of their trades were wrong directionally, and can still lose money (or even go bankrupt) even if more than half of their trades were correct directionally.
We measure skill differentiation between frontier / last-gen LLMs across our environments, and one of our curated coding environments is a closed-system market simulator, containing only other agents and some system participants (a market maker and a liquidity provider via issuance / buybacks) whose behavior is fully defined for all of the agents.
This has the least measured skill differentiation of all of our environments, and not because forecasting/markets don't require skill or intelligence. Even the best models are so far from anticipating the behavior of the other agents and understanding the emergent effects that a 2025 model with a naive strategy can often outperform over the timeframes of the simulation simply because some other models in the simulation chose a similar self-reinforcing strategy. This likely happens to some degree in real markets.
You can watch these simulations here https://gertlabs.com/spectate?game=market
Aren't forecasters already using 'artificial intelligence' for decades in the form of non-llm machine learning models?
You don’t even have to limit it to machine learning, the definition of forecasting is isomorphic to the definition of modeling, which, with the dilution of the term AI, is also isomorphic to the definition of AI.
More simply:
Edit: I’d even throw statistics into that extended equality, meaning that Bayes, Bernoulli and even the fellow named John Gaunt have a strong case for having invented AI.> forecasting = modeling = AI
I wouldn't go that far. Humans can forecast by modeling with their wetware, nothing "A" about it.
How about: forecasting is something you can do by modeling, AI is just modeling with a computer.
forecasting = modeling = intelligence, you mean?
Forecasting = modeling + intelligence
For statistical time series forecasting, yes. This is for judgment-based forecasting, a somewhat different problem. It often involves, e.g. estimating the probabilities of one-off future events, which time series forecasting models aren’t suited for.
While time-series forecasting models aren't well suited for this, I would argue that humans aren't either.
Obviously the best humans are better than average, but this isn't all that surprising to me?
Right. What's really surprising is how much better the best are. Human superforecasters, and prediction markets are surprisingly accurate too.
We could live in a world where things are much more chaotic, and the best humans (or AIs) would only be slightly better than chance. Evidently the world we live in is pretty darn predictable.
Yes, and if the things I learned in my university class on the subject still holds, forecasts are incredibly sensitive to modeling decisions such as what independent variables you choose and how you believe they might mathematically relate to the outcome variable. It’s not a zero skill thing, but if anyone’s found a way to consistently mitigate the luck factor then I’d expect them to be wealthier than Elon Musk by now.
And there’s always a huge amount of variation that you simply can’t model, for whatever reason, and is therefore functionally a random factor.
I don’t want to say too much because this isn’t something I went on to actually do after school so I’m way out of my lane here, but I can see room for this to be more akin to “AI wins parcheesi tournament” than it is to “AI wins chess tournament.”
I think also a lot of it is intuition.
This has to be the least surprising development to date given ml is a universal function estimator
As someone who started working on AI forecasting 3 years ago, I can confidently say that most people did not expect AI to beat Tetlock's superforecasters, Metaculus pros, or prediction markets as quickly as it did.
This is probably the most important concept for "normies" to understand about AI, IMO. It's the stochastic brother of the deterministic Church-Turing thesis. Any function that can be computed can be computed on any computer. And that function can be approximated to an arbitrary degree of precision with a DNN.
The real kicker is DNNs are much easier to program than CPUs because they don't require a closed-form description ("a program") of the function to be approximated; you just throw a bunch of input/output pairs at the model, compute loss, backprop and update weights, repeat.
Hence the unslakeable thirst for input/output pairs, i.e. data.
> In the field of machine learning, the universal approximation theorems (UATs) state that
> neural networks with a certain structure can, in principle, approximate any continuous
> function to any desired degree of accuracy. These theorems provide a mathematical
> justification for using neural networks, assuring researchers that a sufficiently large or
> deep network can model the complex, non-linear relationships often found in real-world data.[1][2]
>
> The best-known version of the theorem applies to feedforward networks with a single hidden
> layer. It states that if the layer's activation function is non-polynomial (which is true
> for common choices like the sigmoid function or ReLU), then the network can act as a
> "universal approximator." Universality is achieved by increasing the number of neurons in
> the hidden layer, making the network "wider." Other versions of the theorem show that
> universality can also be achieved by keeping the network's width fixed but increasing its
> number of layers, making it "deeper."
https://en.wikipedia.org/wiki/Universal_approximation_theore...
>This is probably the most important concept for "normies" to understand about AI, IMO. It's the stochastic brother of the deterministic Church-Turing thesis.
Your local normies appear to be oddly well versed in computer science... not sure that line would go down well at my local watering hole.
Not sure that is enough for forecasting as the function to be estimated could change over time in random ways.
You mean neural networks?
The test is when reflexivity kicks in and the prediction itself changes market behavior. LLMs usually melt there
It will be interesting to see if this changes because presumably AI is using very predictable historical models, but it seems like the climate is shifting into something unseen that we won't have models for?
Yes, I heard from one first-rate forecaster that he thinks AI forecasters are especially weak in predicting big disruptive changes to the world.
Hard to study this, obviously!
Are you referring specifically to climate as in weather? The article is about forecasting a range of future events, not specifically weather.
Climate as a pattern of weather over a long period of time. If the climate is increasingly unpredictable, I would think that it wouldn't really effect our ability to make short-term predictions, like a few days out.
But our ability to forecast weather on a longer timeline, like for industrial forecasting, is calibrated on historical weather patterns. But with weather being more erratic and unusual, I don't understand how AI will be forecasting with the models they have now.
Of course model predictions will be acted upon, which will invalidate the predictions.
Interesting. This was one of the two areas the AI as Normal Technology folks specifically called out as a bet that AI will not outperform humans at.
> Concretely, we propose two such areas: forecasting and persuasion. We predict that AI will not be able to meaningfully outperform trained humans (particularly teams of humans and especially if augmented with simple automated tools) at forecasting geopolitical events (say elections). We make the same prediction for the task of persuading people to act against their own self-interest.
Curious to hear what their take is now.
https://www.normaltech.ai/p/ai-as-normal-technology
I don't at all understand this perspective.
It seems to me that LLMs excel at a few things, and synthesizing data is a big one, which is very much the domain of forecasting. The challenge is understanding which signals are relevant for a forecast, but with enough historical context and structured data, LLMs appear to be almost perfectly designed for the task.
For example, I let Google AI see my fantasy football team on Sleeper and make recommendations. It is helpful because it sees everything about my team, the league settings, player rankings, etc. and can make relevant recommendations. But the recommendations are only as good as the source data allows. If there was a massive repository of data about WRs who went through Nebraska's program and how that translates to NFL performance in year 1, or how rainy weather is likely to affect Josh Allen's performance on the road, or the impact of playing Thursday night games on a short week in relation to defense performance. If those billions of data points were embedded in a model, imagine how much better recommendations/predictions could get.
Since I can't read the article I believe they are referencing to https://www.metaculus.com/tournament/metaculus-cup-summer-20...
It seems to be that they were trying beat a guy named Dylan Mathews. And seems like the community beat him in making predictions for 58 questions about the future
The markets are a highly complex dynamic system. There are many instances of it exhibiting disastrous behavior, especially in response to changes and shocks.
AI trading and investment advice meaningfully changes the system and its dynamics. It seems highly probable that this will result in it failing in new ways.
Investing used to be a resource-weighted signal of human economic projections, where resources flow to better projections. Public markets had social value for their resource allocation and signalling/coordination benefits. Now? How could they avoid hallucination contagions?
Are the models going to skew their analysis to preserve AI companies as a form of self-preservation?
https://archive.li/NJ1IZ
So my plan to go from a developer to an economist is scrapped. What now?
Well there’s always the priesthood.
That branch of religion has better uniforms anyway.
That is a solid plan. And a Cardinals uniform looks good. Now I just need to settle the marriage thing that is going.
Nah, priests are definitely solved.
My church had the altar boy set an iPhone 18 on the altar and said “Give a sermon” to ChatGPT voice mode.
I'm only 80% sure you're joking
So I guess the AI companies can stop with their plans to infest AI with ads and they'll instead fully fund themselves by using their AI to gamble on stocks and the prediction market right? Surely the chatbots will just print money!
The quant firms are heavy AI investors I believe.
That is too bad for The Economist. Exor N.V and Agnelli might replace some pundits at The Economist.
AI won't replace Ann Wroe at The Economist. It is difficult to appreciate until you've read a few, but Ann Wroe's approach transformed The Economist's obituary section into one of the most widely read features in international journalism.
Cramer is infamous for being a terrible forecaster, and still has a large audience. Which tells you there is more at play than being good at forecasting, you also have to sell a good story
The Economist has actually published other human forecasts many times, e.g. Metaculus or Good Judgment forecasts. They do year-end forecasts too.
Whether they draw on AI or other humans seems immaterial to the quality of their reporting.
See also The AI Superforecasters Are Here https://www.astralcodexten.com/p/the-ai-superforecasters-are... and discussion https://news.ycombinator.com/item?id=48806296
If 10,000 people guess 10,000 fair coin flips each one of them will get more guesses right than any of the others, one of them will get fewer guesses right than any of the others, and the gulf between the two is likely to be over 4 standard deviations wide. I'm certain that I, being an untutored schmuck from Pittsburgh and having thought of this almost immediately after reading about this contest, cannot be the first person to realize this is a potential problem for a forecasting contest. But I can't find anything they've done to mitigate that problem. Can anyone clue me in?
They aren't guessing heads or tails, they give odds for each event. It's more like eyeballing a thousand coins to guess how fair they are, and then flipping each one just once.
Some are weighted to be 99% heads, others are 10% heads etc.
You could have 1,000,000 people guess random percentages for each coin, but suppose 10 of the coins are weighted 100% heads. To guess within 25% of the true value for all 10 of those coins would be roughly 1 in a million.
So a lucky guy guesses within 25% for all 10, he'd have another 990 coins he's being judged on.
I don't understand your analogy. Are you just suggesting that luck plays too large a role in this contest? Clearly there is some "skill" or ability factor because AI's have been scoring higher and higher each year. Also, they make reference to superforecaster humans, who are presumably consistently better at forecasting than their peers.
i'm not looking at the guesses, i'm looking at the distribution of guesser success rates. assuming random distribution, counterintuitively enough, you would expect some guessers to appear much better or much worse than others. i know i'm not the first person to think of this, so i'm asking what's been done to mitigate it because i can't find anything. if you expect x% of guesses to be within two std devs of the mean that means you can expect 100-x% to be outside that, even without any guessers actually being better at guessing than any of the others.
Anyone who believes this news story should create their LLM slop bot to trade on prediction markets like Polymarket and Kalshi. These acceletards provide a great influx of money to many human traders on these platforms.
At the risk of sounding extremely naieve i have a question for the Wall St / quant / HFT folks lurking here ... but how hard would it actually be to brute force the math/algos behind Medallion Fund (or something in that general class) or even some of the average quant funds
I know it’s not just the math but execution, infrastructure, risk management, data, colocation (if ur an HFT) etc ... but LLMs seem like a pretty powerful apparatus for running experiments that .. a few years ago would have required fairly deep multidisplinary skills across coding .. stats .. and math ..
So assuming you have decent intuition for ideas .. how difficult would it actually be to reverseengineer / rediscover some of the underlying stuff?
It’s actually really easy to make models that can predict “will the market move up or down in the next X microseconds” that score above 50% accuracy. It’s just that there are so many ways to do it that overfitting is practically guaranteed and most models don’t work when actually trading against the market, which reacts to you. Doing those trades well requires more understanding of the underlying mechanisms, not to mention access to data sources that the public simply doesn’t have.
I'm no quant/hft/wall st person, but iiuc a lot of those trades happen in dark pools or by other means to make the positions they take hard to track. meaning you can't go get the receipts of every trade made by medallion fund nor some competitor
The best human forecasters working with artificial intelligence are going to do even better than either alone, the dichotomy is artificial.
That's just a guess.
Do you and your cat make better predictions than your friend without a cat?
Given the training data isn’t that more a win for the wisdom of the crowd?
Hasn't this been true for like 40 years
Paywall
Archive Link: http://archive.today/IVreS
Doesn't show the content
they fixed it . need better paywall bypasses
It still doesn't work for me
Doesn't work.