I'm currently dealing with the following problem.
I'm using lasso regressions to model hedge fund returns and understand their exposures. The idea being, that if their returns are simply due to factors, there is no reason to pay 2&20 and one should simply buy those factor exposures from the cheapest provider (etf, smart beta fund, etc.).
Running the regressions and looking at the R^2 helps but seems unsatisfactory to me as regressions with enough possible factors will overfit and spuriously explain everything.
Recently I've been trying to cross-validate by training the regression on say 8 years of data and then testing the predicted results for 2 or more out of sample years. I feel there has to be a more rigorous way than this naive leave one out approach, especially keeping in mind that many managers have short track-records.
Any advice? Would something like k-folds work well for this type of time-series data?
p.s. I'm using R by the way so any applied suggestions would be helpful.