The use of artificial intelligence and machine learning by hedge fund managers has grown along with the practice's increased application across many other industries. As a subset of systematic strategies, AIML funds capitalized on the explosion in the volume of data and the real-time speed in which it can be processed to not just act on market signals but predict them.
A larger share: Fund launches fell sharply in 2019 from previous years. However, AIML funds' share of that group has been growing. Equity and relative value have been popular strategies among these managers.
Sniffing out downturns: Avoiding acute downturns has shown to be a strength of the strategies, as the AIML subset has been flat to positive when equities fall. Short-term upside capture, however, has been more challenging.
Best under stress: AIML funds outperform public equities most when volatility is high and mispriced opportunities are more frequent. AIML funds show better diversification benefits relative to the broad hedge fund class.
Crowded market: Recent data show increased volatility among AIML funds, possibly due to the growing number of strategies and more-crowded trades. The increase has caused a dip in portfolio efficiency.
*MSCI ACWI index. **Eurekahedge AI Hedge Fund index. ***Eurekahedge Hedge Fund index. Sources: Bloomberg LP, Eurekahedge Ltd., Preqin Ltd.