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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.

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Both of the references below talk about specifically the problem you are looking at and discuss methodologies that might of interest.

  1. There has been a lot of work done on replicating hedge fund returns and studying whether they can be explained by common factors. The seminal paper on this topic was written by Andrew Lo

  2. Also Andrew Ang, when he was at Columbia did a great job of explaining hedge fund returns via common factors in his paper, that kinda started the smart beta revolution.

w.r.t. R packages - the caret package is the one you want to look at. Here is a reference on caret that talks about using caret for data partitioning, linear regression and cross validation .

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