here’s a slightly wonky thread that people who use post-nominal letters will not like: let’s talk about real-time learning in quant-driven strategies and how to do it in practice, because a lot us do this as a result of real-world constraints. its basically TA with a dress-code.

so lets say you found a nee factor, but data is limited becuse it is new-ish? how the hell do you implement it without waiting eons in market-time? first: in small size because distributions are unknown. second: you update the calibration parameters dynamically as you go.

sometimes i am asked why i favor matching assets with volatility estimators instead of betas:
1) volatilities are not stationary but they undergo relatively synchronized regime changes
2) exponentially weighted vols are more responsive to changes in current conditions

3) betas are not stable in shorter terms and identifying changes takes longer and in the short term you get measurement errors if your sample is driving your benchmark
4) honestly the numerical methods are just simpler and i’m a dumdum so it makes it easier for me

5) for long-only baskets of different assets it avoids painful assumptions which may be wrong about correlation, which is good when you know your sample is low quality
6) for long-short baskets of like assets it avoids painful assumptions about intercepts (and therefore betas)