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
in order for your model to accomplish that, you would get very rich.
there will also likely always be more. in the limit in order to get an edge your model would start to infer insider information. for example, it's common knowledge by now that satellite imagery is used to measure car numbers in parking lots, that's a proxy for insider information.
so it's not even so much the model as it is the data.
even being able to forecast weather better than publicly available methods can be leveraged to gain a significant edge.
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.
> you should put money into them to get higher returns in the future.
Higher returns than what? I thought the whole point of buying broad market index funds was to simply get the market returns. For this thesis to make sense, you simply must assume that companies, in aggregate, make money - not that any particular company will follow past performance. If you don't think so, then what are you doing buying equities?
there will also likely always be more. in the limit in order to get an edge your model would start to infer insider information. for example, it's common knowledge by now that satellite imagery is used to measure car numbers in parking lots, that's a proxy for insider information.
so it's not even so much the model as it is the data.
even being able to forecast weather better than publicly available methods can be leveraged to gain a significant edge.
There's definitely alpha out there, but I wouldn't want to make it my job to look for it
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.
Higher returns than what? I thought the whole point of buying broad market index funds was to simply get the market returns. For this thesis to make sense, you simply must assume that companies, in aggregate, make money - not that any particular company will follow past performance. If you don't think so, then what are you doing buying equities?