The investment team uses artificial intelligence to drive the investment process and human intelligence and intuition to perfect it. Data science driven models identify attractive investment candidates by analyzing fundamental and valuation-based attributes which have shown a quantifiable edge in terms of stock selection. AI-driven crowd behavior models then identify those stocks that are experiencing increasing investor interest by performing fractal analysis on volume, price, and volatility data.

The investment team then applies traditional detailed fundamental analysis to the identified opportunity set to create a focused portfolio of timely investment ideas that are diversified across industry sectors.

Artificial Intelligence makes the fundamental research process more efficient and scalable by allowing the investment team to focus on companies which are consistently growing their intrinsic values through time. Employing data science tools also helps protect returns by identifying “value traps” and stocks that are about to experience a negative trend change. The resulting process is expected to drive excess returns by solving for the pitfalls of both traditional fundamental analysis and quantitative strategies while utilizing the best attributes from each discipline.

Portfolio sector weighting decisions are based on the AI-driven crowd behavior models and the number of attractive investments within that sector, but the team targets a diversified portfolio and adheres to certain allocation limits. Preserving capital in difficult markets is the key to generating high compounded rates of return. Thus, the strategy employs a multi-faceted risk management process including intensive fundamental research, diversification, the use of artificial intelligence models to exit positions or avoid individual securities which lack sufficient investor interest, consistently applied sell disciplines, and, when appropriate, the use of various hedging vehicles including options.