HFND holds a portfolio with long and short positions in 30-50 underlying ETFs and future contracts with similar return characteristics as the hedge fund industry gross of fees returns. The strategy hinges on the premise of outperforming the hedge fund industry due to relatively high fees and expenses compared to the funds lower operating expenses. The sub-adviser uses machine learning algorithms that best match the reported monthly gross of fees returns of each hedge fund style. The fund then aggregates each style portfolios positions to build a total hedge fund industry model. Each style portfolio is weighted based on relative asset levels in each hedge fund style. The portfolio may include passively managed ETFs with exposure to commodities, fixed income, and equities across various sectors and styles globally. The actively managed fund does not invest in hedge funds, replicate their direct underlying holdings, or engage in certain activities permissible for hedge funds.