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It used to be that environmental, social and governance investing meant negatively screening of so-called sin stocks. This is no longer the case, but ESG is still struggling to gain a firm foothold in quantitative investments.

Enter big data and artificial intelligence. According to the July edition of the Cerulli Edge, AI is enabling investors to access huge amounts of information from objective sources, bringing greater frequency, granularity and real-time analysis to ESG investments.

“The use of big data and AI is radically transforming data gathering,” Justina Deveikyte, associate director of European institutional research at Cerulli, said in a statement. “These changes have opened up ESG investments to quant funds, which are busy developing new algorithms to systematically evaluate companies.”

A recent report found that wealthy younger Americans are intent on leaving legacy influenced by socially responsible spending and investing.

Up to now, ESG has been unable to fully capitalize on quantitative investments, mainly because weaknesses in the data have made it difficult to build strategies, according to Cerulli.

Unlike financial reporting, no universal guidelines exist to steer or compel corporate ESG reporting. Piecemeal information and incomplete data are the norm.

Deveikyte noted that data limitations have affected quant investments in several ways. Big companies, for example, are better at disclosure than small ones. Because most ESG models penalize nondisclosure, this can result in a large-cap bias in portfolios.

But research shows this is changing. A survey of 461 asset managers around the world with approximately $5.4 trillion in assets under management found that while 55% considered lack of robust data a barrier to adoption of ESG today, only 15% said that would be true two years from now.

According to Cerulli, providers are currently drawing on unstructured data from multiple sources to analyze thousands of companies every day, including news outlets, social media, nongovernmental organizations and Google trends.

Investors can now measure intangible factors that account for up to 80% of a corporation’s value — such things as brand value, innovation levels or employee satisfaction.

How does this work in practice?

Cerulli reports on one asset manager that uses raw and unstructured data to build an initial “input layer,” which combines companies’ own reporting with external information gathered from more than 50,000 sources in 15 languages to create a daily view of some 7,000 companies around the world. The technology filters out fake news.

This vastly improved access to data is changing how asset managers integrate ESG, enabling them to come up with new investment themes, Deveikyte said. In one trend, it is allowing more long-only strategies.

In another trend, the flow of data is introducing more short-term, reactive strategies. These were traditionally difficult in ESG because of the slow pace of change, accentuated by the lag in reporting data, according to Deveikyte.

Cerulli research in the U.S. concluded that, although infrastructure existed for the likes of Amazon and Google to enter the asset management market, the attendant complexities in serving retail investors would present such firms with big challenges in terms of scaling their core businesses.

The research also found that a growing number of Asian asset managers were using AI technologies in portfolio construction or exploring its use in their investment processes.

However, only large players are able to afford the high costs of building quantitative teams and applying AI, likely leaving smaller companies in the competitive dust. This could fuel consolidation in the industry, Cerulli said.

— Check out New DOL Bulletin Muddies ESG Waters on ThinkAdvisor.