You've read it somewhere, run PCA for "statistical factor analysis"; but material on this is either so shallow that it's meaningless (run pca and the eigenvectors are factors), or so dense that you'll need a PhD in Statistics to parse it.
This is the most information dense article on why PCA can actually extract factors, and how to reason about it.
Happy to share the article with some people who comment and retweets!
If you join a statarb team, you are essentially trying to run variants of mean-reversion (MR) strategies.
The catch is this that traditional MR (betting that gross returns will revert) is negative after-cost since 2010.
So, how have statarb teams adapted?
Simple, they've learnt how to make mean-reversion strategies in a different "space". "Gross returns" are an example of a statarb "space".
Today, i want to talk about a really novel "space" that still produces meaningful performance even till today. Perhaps some of you may have a clue, but the hint is that it involves rethinking factor decomposition!
If you'd like to find out more, head over to the bad place AND as usual, I will give out random free reads to retweets + comments on THIS POST.