A new paradigm in the US calls for a new approach: Can machine learning deliver a diversified alpha stream?

The Trump administration brings with it opportunities, challenges and uncertainty for global investors and is leading both passive and active allocators to rethink their US equity allocations. Many active portfolios contain unintended structural active biases to certain factors and top-down risks, which can lead to positive performance outcomes over the longer term. But this can also mean they are susceptible to prolonged periods of underperformance over the short to medium term. Federated Hermes MDT Advisers believes there is a better way to design portfolios, one that seeks to mitigate the impact from structural style biases and deliver more alpha consistency regardless of the market backdrop. MDT’s investment process identifies a diversified range of alpha sources for investment consideration and equips our portfolios with the potential to add value regardless of the market environment. This diversification of alpha sources combined with a disciplined, risk-efficient portfolio construction process, provides the opportunity to deliver more consistent, durable, all-weather performance outcomes for clients with minimal alpha correlation to traditional approaches. Join Federated Hermes MDT Advisers to explore: - How sophisticated predictive modelling, including machine learning tools, can be used to identify greater breadth of alpha sources for portfolios. - Portfolio design that seeks to mitigate the impact from structural style biases and deliver more consistency in relative performance. - The case for using The MDT All Cap Core US Equity strategy in a building block approach to US equities.