We assess the performance of a machine learning-based, long/short factor timing strategy in the large-cap U.S. equity market.

We explore the changing landscape for dividend yield in the U.S. equity market.

Clarifying the impact of a net-zero commitment on equity portfolios.

We uncover fundamental value using a systematic approach in corporate bond markets.

We apply a form of unsupervised machine learning to derive the probability of being in a stressed economic state.

We leveraged a dataset spanning 150 years to unveil investment returns across inflationary scenarios.

Is the 2023 growth rally a stock rally, or merely a style rally? We review the recent growth rally to see what exposures investors are really paying for.

Adaptive factor definitions capture underlying signal momentum while maintaining persistent factor exposure.

Unsupervised learning algorithms may be a useful way of neutralizing anti-growth biases.

Passive ESG investing has become increasingly active. It’s time for a rethink.

Equal Weighting creates more problems than it solves. There are better ways.

Factors work in Emerging Markets too. Explore the return and diversification benefits.

Analysis reveals how beta asymmetry works – and when it doesn’t.

A new volatility regime has delivered more return for less risk.

The benefits of style factors come with the cost of cyclicality. Diversification is the key to staying invested.