Awhile back, Simon Thornington posed an interesting question regarding performance attribution at the hedge fund level. The basic idea was, assuming for the 1337 Street Fund, a workflow similar to that described on A Professional Quant Equity Workflow is used, then how can the relative performance of an individual algo (an alpha) be determined, such that the author can be compensated accordingly? Assuming that the individual licensed algos are combined as a simple weighted sum of alphas, would this performance attribution technique allow one to sort out the relative performance contributions of each algo, even after the optimization step? Or maybe the idea is to apply all of the optimization and risk management at the level of individual algos, and just weight them by capital invested in each (in which case, the performance attribution is simple)?
It seems that authors should be paid based on the attribution of their alpha to the overall return of the fund, rather than their algo return. So, if the performance attribution technique applied in the risk model works, then perhaps it could be used at the hedge fund level, as well, to determine the relative weighting of licensed algos in the fund?
I think this all goes out the window if ML is used to combine algos, since as I understand, everything will get totally jumbled up point-in-time, and so it will be impossible to tell the relative contribution of each licensed algo in the combined alpha.