I've noticed in my backtests that "shrinking" the expected returns vector towards zero tends to improve the performance. This has led me to investigate shrinkage methods for the forecasts/expected return vector vs the traditional "shrinkage" as applied to the risk/covariance matrix estimation.
One structured way to do this is the Bayesian approach - which seems to lead to Black Litterman. There is some advice on shrinking the expected returns vector here
but I'm wondering if people tend to perform Black Litterman in an online sense as in portfolio rebalancing.
E.g. this would mean using your previous weights/portfolio positions as your prior and updating your prior with your new expected return forecasts at the next time step.
Is this a common approach/use case of Black Litterman?