As thematic exchange-traded funds gain popularity, benchmarking their performance grows in difficulty as market data, fundamentals, and corporate actions provide only so much insight into the themes on which the ETF are based.

“We know that robotics is going to be really significant for the economy in terms of automation,” Matteo Andreetto, CEO of Stoxx told Markets Media. “But there is no such thing as a robotic sector. So you really have to go down and analyze a lot of data.”

For the past two years, Stoxx has built an infrastructure to support future thematic indexes that will address the global macro themes of demographic changes, digitization, and global warming. As a result of the build-out, the vendor has doubled the amount of data it consumes annually and has incorporated more alternative and “big” data into its calculations. It also looked to machine learning as well as collaborated with AI-startup Yewno to develop the necessary infrastructure to consume and analyze the ever-expanding wealth of data.

When the company developed it recently launched AI Index, it had to identify not only which companies brought AI-related tools to market, but which ones would benefit from the implementation of those tools into their workflows, according to Andreetto.

To identify the AI-tool providers, Stoxx analysts examined each candidate’s FactSet Revere sector classification, which provided a significant level of granularity regarding a firm’s composition and revenues.

For the larger pool of companies that could see a positive impact from using AI, Stoxx turned to machine learning. The AI engine examines patent filings related to AI, which can indicate leading AI innovator and adopters.

“To design the best possible thematic exposure or any type of non-traditional exposure you need to turn raw data into ‘smart’ data,” said Andreetto. “And no one controls data. You maybe can own a little bit of data, but the data that you own cannot be all the best data available.”