There is no such thing as a generally best model due to the no free lunch theorem. What works in hedge funds will be bad in other areas that need less or different inductive biases due to having more or less data and different data.
Some funds that tried to recruit me were really interested in classical generative models (ARMA, GARCH, HMMs with heavy-tailed emissions, etc.) extended with deep components to make them more flexible. Pyro and Kevin Murphy's ProbML vol II are a good starting point to learn more about these topics.
The key is to understand that in some of these problems, data is relatively scarce, and it is really important to quantify uncertainty.
I know next to nothing about this. How do people make use of forecasts that don't provide an uncertainty? It seems like that's the most important part. Why hasn't bayseyan statistics taken over completely?
Bayesian inference is costly and adds a significant amount of complexity to your workflow. But yes, I agree, the way uncertainty is handled is often sloppy.
Maximum likelihood estimates are very frequently atypical points in the posterior distribution. It is unsettling to hear people are using this and not computing the entire posterior.
For example, satellite imagery of trucking activity correlated to specific companies or industries.
Its all signal processing at some level, but directly modeling the time series of price or other asset metrics doesn’t have the alpha it may have had decades ago.
