I have a data set which consists of > 500 hedge funds, their historical monthly returns, and their benchmark (index) monthly returns. The number of data points (# of monthly returns) differs from a fund to fund (can be as low as 10 and high as 200).
I am wondering if there are some machine learning, advanced statistics, or other quantitative techniques to identify "good" and "bad" funds. I've been googling and I can only find basic statistics such as sharpe ratio, information ratio, etc and compare the statistics between fund and index.
I am not trying to predict the funds' returns. I am only interested in evaluating their returns and risk, using quantitative techniques.