Although quantitative methods for fund analysis have been around for decades, they tend not to work very well on hedge funds. They need lots of data, and they require funds to behave consistently over time.
Hedge funds, on the other hand, often have short histories. They report returns infrequently and they source their risks from numerous factors. They usually have highly dynamic and non-linear risk exposures. All of this makes empirical analysis very difficult.
Our algorithms overcome these obstacles by augmenting traditional statistical techniques with advances in machine learning. They often produce substantial improvements over conventional approaches.
Why should you care? Our methods can help you evaluate more funds, faster. They allow you to expand your investable universe and design more efficient portfolios. They also facilitate analyses that have previously been either difficult or impossible to perform, such as:
- Identifying sources of risk and return
- Generating near real-time, intra-month performance projections
- Simulating returns prior to a fund’s inception
- Predicting future returns, correlations, and volatilities
- Determining the value of any particular fund to its portfolio
- Optimizing portfolios based on factor exposures