MSCI has updated its equity factor models with three new inputs – sustainability, crowding, and machine learning – which the index provider believes can help better explain the drivers of portfolio risk and return.
Mark Carver, Head of Equity Portfolio Management and Equity Factors at MSCI.
Sustainability includes both a broad ESG factor and a separate carbon efficiency factor based on a company’s emissions relative to its size.
Crowding measures the ‘bubbliness’ of stocks and portfolios by assessing how they are priced relative to their own history.
Machine Learning leverages data science and natural language processing to understand and capture the non-linear relationships between different factors.
MSCI’s equity factor models are designed to provide institutional investors with enhanced transparency into their portfolio characteristics or help them construct portfolios across new and familiar factor dimensions. The firm offers both factor models designed for long-term investors as well as for traders with shorter investment horizons.
MSCI is a leading provider of factor indices for the ETF industry and it is likely that the newest models will form the basis for future product development.
Mark Carver, Head of Equity Portfolio Management and Equity Factors at MSCI, said: “Investors have told us repeatedly that the new risk measures in these models, combined with the introduction of sustainability factors, are crucial for an evolving investment landscape.
“We are excited to introduce these innovative models and believe they enable clients to better understand the drivers of their portfolio risk and return, construct differentiated portfolios, and effectively respond to changing market dynamics.”