Cross-Validating Hedge Fund Factor Models on Time-Series Returns
Summary
The document considers whether hedge fund returns can be explained by static exposures to common investment factors. It warns that a high in-sample fit, such as a strong R-squared from a lasso regression, may be misleading when many candidate factors allow overfitting. The proposed basic check is to fit on earlier years and evaluate predictions on later, held-out years, rather than judging the regression on its training sample.
The response points to prior research on replicating hedge fund returns with common factors and suggests using a general-purpose R package with data partitioning and cross-validation tools. It does not give the cited studies’ methods, specify a time-series split design, or show empirical results. The question of how to validate reliably with short manager histories remains open; ordinary random folds may not preserve chronology or avoid leakage, so the excerpt is guidance toward further research rather than a full validation protocol.
Key ideas
- In-sample regression fit can overstate how well common factors explain hedge fund returns.
- A time-ordered training and holdout split tests whether estimated exposures predict later returns.
- Lasso is the modeling approach described, with many candidate factors raising overfitting concerns.
- The answer points to hedge fund replication research and cross-validation software but supplies no detailed procedure or results.
- Short track records make validation design difficult, and the excerpt does not resolve that challenge.
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Full text
# How to Cross-Validate whether fund returns are due to static factor exposures? # How to Cross-Validate whether fund returns are due to static factor exposures? 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. ## Answer by Swagato Acharjee (score 1, accepted) https://quant.stackexchange.com/a/31547 Both of the references below talk about specifically the problem you are looking at and discuss methodologies that might of interest. - 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 - 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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