Global benchmark Wilshire Indexes and Rayliant Global Advisors have launched the FT Wilshire NxtGen Index Series by harnessing machine learning and artificial intelligence (AI).

The index series aims to better capture equity premiums from a robust universe of factors potentially leading to more stable systematic return harvesting.

While changing market dynamics are increasingly disrupting global investment, technology has opened the door to new index construction methods, the companies say, making possible new diversified and global approaches to smart beta.

Employing machine learning and AI to construct a modern, robust multi-signal index series, the index series spans developed and emerging global equity markets, the companies point out, offering investors a comprehensive, investable, transparent and modular alternatively-weighted index series.

The index series uses robust machine learning and AI models of expected return within a mean-tracking error framework. More than 100 stock-level fundamental and market characteristics from 12 major factor categories are used to estimate quarterly future stock risk-adjusted returns.

This list of fundamental and market characteristics is in close alignment with those described in the academic literature and are reviewed on a quarterly basis. This approach mitigates the impact of single characteristic underperformance, provides access to a diversified universe of fundamental characteristics to identify an optimal allocation and maximizes expected risk-adjusted return.

“AI and machine learning have emerged as the key to advancing and unlocking the next generation of smart beta,” shares Jason Hsu, founder and CIO of Rayliant. “The index series innovative construction approach, which leverages our joint expertise, provides global investors with a modern replacement to their legacy smart beta allocations or potential use as a dual-core approach.”

Mark Makepeace, CEO of Wilshire Indexes, adds: “New technologies together with improvements in data science allow us to implement classical financial theory more effectively. This is an exciting development and will help global investors continue to explore ways to use AI and machine learning to drive value, capture new opportunities across markets and differentiate themselves from competitors.”