How AI can help demonstrate hedge funds’ ESG edge to investors
Trevor Headley | head of Hedge Fund Product Management, FIS
August 13, 2021
Demonstrating the value of ESG indicators within their portfolios is becoming a key task for hedge fund managers of all stripes and strategies. FIS and Hedgeweek recently hosted a webinar that examined how AI technology can help firms rise to the assortment of challenges this presents.
In the discussion, it was shared that ESG funds have attracted some US$340 billion of net inflows from investors over the past two years. Recent studies suggest up to three-quarters of ESG funds’ outperformance is down to quality metrics.
With that in mind, the discussed moved to how managers can better use data analytics and data tools to demonstrate to investors how they can generate outperformance and boost returns using ESG indicators. This includes not only improving their investment frameworks to better screen companies, but also strengthening the ways in which managers track and report ESG factors in their portfolios.
Observing how ESG factors should form part of the investment hypothesis when entering into a trade, Trevor Headley, VP, product management, hedge funds at FIS – who has worked in technology offerings for hedge funds’ front-, middle- and back-office functions for some 15 years – said accessing timely information is key.
While a “rich ecosystem of data providers” allows hedge fund managers to utilize technology to bring data into their platforms and develop insights, this should be complemented by certain unstructured and alternative datasets.
“That unstructured data, or alternative data, is what is really going to give you the edge in terms of performance,” he explained, adding that AI can serve as an enabler in this area. “Having that real-time view of how sentiment is potentially changing as it relates to some of these factors is incredibly important.”
The discussion also explored how allocators’ assessments of managers’ research and screening processes is rapidly evolving beyond the standard measurement of sustainability factors within company practices.
“As fund selectors, we’re always looking for outperforming funds and looking for why a fund is outperforming,” said Sophie Outhwaite, head of equities and responsible investing at London-based Stanhope Capital. “Our starting point is that we are desperate for ESG-integrated or ESG-focused long/short funds.”
Expanding on this point, Outhwaite believes that AI will become an invaluable tool in the drive for outperformance particularly within the short-selling component of hedge fund strategies.
“It may be a bold statement, but I think it’s relatively easy to put together a long book of companies that you think will help the future arrive. The stock selection may be hard, but you know the themes: We have a very good idea of some of the sustainable, future-oriented, responsible themes now,” she observed.
“But on the short side, the difficulty with most ESG factors is timing. You don’t know when regulation is going to suddenly heat up. You don’t know when investor sentiment is suddenly going to turn. AI gives you the tools to be nimble.”
Arnaud Langlois, portfolio manager of the 1798 TerreNeuve Fund at Lombard Odier Investment Managers in London, has been exploring sustainable investing for some 15 years, having earlier led the ESG research team at JP Morgan in 2005.
“The desire to demonstrate how ESG research can play a big role in delivering alpha has been at the center of what I’ve been doing both on the sell side and the buy side for the last 12 years running money in the hedge fund space,” Langlois said. “We’ve built our proprietary dataset that captures that. Effectively, we can demonstrate that our stock selection based on our model would have probably played a big role in delivering the alpha we generated last year.”
Elsewhere, the discussion touched on greenwashing, ESG integration and exclusion within portfolios, and some of the potential hurdles arising out of the explosion of datasets over the past few years. Both Outhwaite and Headley highlighted the enhanced reporting capabilities of AI.
“Longer term, we will see technology – specifically AI, and not just related to ESG – moving up the value chain from data aggregation helping towards presenting more detailed insights as to how things are moving and trending,” said Headley.
Langlois added: “I don’t consider myself to be an expert in AI, but I can see the role that can play in the future. Our work has been centered on a few things: We continue to pursue the objective of having a net long exposure to the thematics around decarbonization, so we’ve done a lot of work internally on net-zero targets by 2050, developing some proprietary tools that allows us to calculate the trajectory and footprint of companies.”
Headley cautioned that, while the AI-based tech capabilities have evolved, there are risks, noting there are trading algorithms that often all point in the same direction.
Langlois, meanwhile, warned that datasets by their nature are often backward-looking, adding: “You can’t rely too much on data providers to tell you what the future will look like.”