HFND seeks to create a portfolio with return characteristics similar to the hedge fund industry’s gross of fees returns, and believes the fund may outperform the hedge fund industry net of fees returns by charging comparatively lower expenses. The fund does this by using a proprietary machine learning algorithm to create a portfolio which best matches the most recent month’s returns of each major hedge fund style (such as long/short equity, global macro, event-driven, fixed income arbitrage, emerging markets, managed futures, and multi-strategy). The fund then aggregates these portfolios based on the relative asset levels in each hedge fund style into a total hedge fund industry model.