Machine learning methods have several advantages that can lead to successful applications in active portfolio management, according to a new study.
The study, ‘Machine Learning for Active Portfolio Management’, investigated the performance of a sample of active ETFs that use machine learning in their investments.
Researchers found that performance tends to be “mixed”.
However, nonlinear pattern capture and the ability to focus on prediction through ensemble learning meant machine learning had several advantages leading to successful applications in active portfolio management.
Overall, researchers concluded that machine learning techniques are “promising” for active portfolio management, but investors should be cautioned against their potential pitfalls.
*Read the full feature here.
© 2023 funds europe
