Investment Philosophy

Breadth is extremely important for any quant factor fund investment process - the ability of generate strong risk adjusted returns improves with breadth.

Data mining is the biggest pitfall for any factor driven quant investment methodology, so it is important to incorporate safeguards – empirically derived screens based on intuition and theory that have been tested in-sample and out-of -sample.

Quant factors can be combined using a non-linear stock screening methodology to target market anomalies more precisely.

A non-systematic overlay, if correctly implemented, can generate significant alpha and mitigate risk.

An intuitive and transparent portfolio construction methodology which focusses on maximizing alpha and reducing risk exposures generates superior results to optimizers based on historically calibrated risk models, especially when dealing with exogenous shocks.

Portfolio rebalancing should focus on maintaining appropriate alpha and risk exposures while minimizing transaction costs, particularly market impact costs.