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.
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. 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.
Which means that success in forecasting involves a lot more luck than success in intellectual pursuits. 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, but I can see room for this to be more akin to “AI wins parcheesi tournament” than it is to “AI wins chess tournament.”
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.
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 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!
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?
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?
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
So I can 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!
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.I wouldn't go that far. Humans can forecast by modeling with their wetware, nothing "A" about it.
Which means that success in forecasting involves a lot more luck than success in intellectual pursuits. 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, but I can see room for this to be more akin to “AI wins parcheesi tournament” than it is to “AI wins chess tournament.”
"The AI-driven Market Hypothesis"
Please let me know where I should pick up my Nobel prize.
It's derivatives all the way down
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.
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!
Whether they draw on AI or other humans seems immaterial to the quality of their reporting.
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?