I guess for all we know, a sophisticated ML model like that might already exist in some hedge fund, but they'd be keeping quiet about it so they don't lose their edge.
Day trading securities is a smaller problem than macroeconomic forecasting, probably... but down this path lies Asimov's psychohistory. It's basically forecasting history.
With a more detailed forecast you need a better approach like you described. I just found the limited case easier to reason about.
That's the original proposition I was working with. Obviously, if the amount of published forecasts finite, then the amount of decision changes in response to such publications will be finite too.
Now that I think about it, a forecast that causes nobody to change any plans is pretty useless and is effectively indistinguishable from the trivial "empty" forecast which got to be the unique fixed point of this whole forecasting exercise.
It's all based on trust though, if one forecaster suspects that another forecaster has been hacked to optimize profits for that individual player, the equilibrium is broken.
Even with everyone in the world having an all powerful genie, the greed of a few but powerful can ruin the entire thing.
Thats why people working at central banks like the Federal Reserve will try to downplay risks like inflation to prevent signalling that theyll raise interest rates causing a market correction or businesses from raising prices to deal with it.
I think the cats out of the bag now but they sure would love to stuff it back in the bag.
They invest pretty heavily to make sure their edge is kept a secret.
It's more likely that this information will be withheld by incumbents since it is a valuable pricing signal.
I don't subscribe to it myself. But when you have a system that's state is dependant on the descisions and therefore actions of its participants anything that influences those decisions, i.e. signals, influences its state. Even the actions of individual actors in a market can be treated as signal.
I think of it a bit like Chaos Theory. An investor saw a butterfly flap its wings in Fiji which triggered a chain of events culminating in the DOW dropping 800 points on a day.
Financial black swans don't tend to be random, real world events. We're living through a major real world event now, and the financial markets haven't collapse... though a lot of weird stuff is happening. The 2008 crisis and many others had not real world. Real world links were gradual and fairly financial in nature, an accumulation of debt/risk/leverage/lies/fragility or whatnot over time.
These are more like bugs, design flaws or fail conditions, IMO.
From a financial analyst's perspective they're "black swans" because they are events with a >0% likelihood that (by definition) are not priced into security prices or financial systems.
From a forecasting model POV... who knows.
In any case, forecasting the future of the economy is probably very very hard. If it's possible, it probably has epic sci-fi level consequences.
Central banks are pumping money into the economy at a record rate right now, and still not able to get unemployment down to "normal" levels.
This causes supply chain problems and inflation signals that , unless they get resolved, will force the central banks to pull back most of the stimulus. THAT is when the crash will come (if at all).
If that comes, central banks will have a choice. Double down on the austerity, raise taxes and try to weather the storm, or restart the money printer and risk very high inflation, if not hyperinflation.
My bet is on the latter, as goverment obligations are so high right now that I doubt they have the stomach for austerity.
This crisis is not over....
For instance, the model predicts 2023 GDP to decrease so we take all out measures to cause this to not happen.
Was the model wrong? How would you know?
In this context, the model would need to predict the actions by the Fed but the Fed would be the ones using the model.
So you would also have to predict the Feds reaction to the prediction the model made of the Fed's reaction to the model..on and on.
Such a model can't exist without an solid understanding of human psychology, and that involves trust. Likely, the machine would need to understand this, and spend the first few months/years proving its worth, such as by making those in control of it rich. To make it work, the humans have to place their trust in the machine. Honestly, it's probably better to invent the machine then feed its commands to a real person that people can place their trust into, not unlike Westworld Season 3.
People use machine learning, but nothing this sophisticated.
Outside of the market makers, most of the successful firms are just selection bias.
That was Dark Sky's play - and it was amazingly fruitful. It would even leverage the barometer in most phones. It was a bit disappointing when Apple acquired them since it cuts non-iOS users off, but it was a great app when it was out. For iOS users as of iOS 15 Apple's weather widget is now based on Dark Sky's tech making their app redundant.