The real story here is this wonderful exposition in applying diffusion models to a time series data that is neither discrete nor continuous. It’s always fascinating to see diffusion models applied in different scenarios, same with diffusion language models.
There is no model of the market that can remain stably accurate because the market will inevitably incorporate the insights of any model that is accurate until those insights are no longer accurate
The market has modes and reverts behavior when it switches them. Thus happy bouncy becomes hammered stammered. The prediction models fall hook and sinker for that.
You hear that often, but if you squint your eyes, the entire idea of index funds is just that: they outperformed stock-pickers in the past, so you should put money into them to get higher returns in the future. There's no fundamental index fund investment thesis other than "past performance is indicative of future returns".
That thesis is at least to some extent self-fulfilling, because there's so much money flowing into index funds that prices of all the underlying assets keep moving up, and there's probably not enough money trying to bid against that / arbitrage the excesses away.
A similar thing could happen with AI. Markets are efficient only if the world isn't in some sort of a trance.
That thesis is at least to some extent self-fulfilling, because there's so much money flowing into index funds that prices of all the underlying assets keep moving up, and there's probably not enough money trying to bid against that / arbitrage the excesses away.
A similar thing could happen with AI. Markets are efficient only if the world isn't in some sort of a trance.