Feature – Artificial intelligence
enter. And the third is what we would call the naïve Bayes clas- sification based on Bayesian inference. If you confront the sys- tem with a set of factors, it can predict the probability of a cer- tain outcome. If you don’t have one of these types of machine learning all you have is data,” Beckett stresses. His impression is that most professional fund investors are not yet engaging with AI on a conscious basis. “Are fund selec- tors taking the lead on this? The answer is no. I know only of a few, mainly quant-driven, professional investors with a mathe- matical background who are looking for tools in the market that empower them to incorporate AI in their decision-making process.
“Overall, AI integration happens more as an osmosis. A lot of fund analysts are not fully aware of the change in the way they are doing things,” he adds. “In the same way that 20 years ago, it would have been hard to explain to fund selectors what Morn- ingstar or Lipper would do for them today.” Morningstar on the other hand is already using AI in its fund ratings, which in turn also affects institutional investors who use the data provider. In 2018, it launched a new quant rating system which uses a machine-learning model that enables it to rate six times as many funds than an analyst would. Gavin Corr, director manager selection services at Morning- star, explains that the tool helps Morningstar to tackle the effects of a rapidly growing asset management industry. Morn- ingstar’s 125 fund analysts currently screen funds based on their investment process, people employed, past performance, the parent company and price, subsequently awarding funds with a bronze, silver or gold rating. The machine learning
model replicates this screening process across a broader fund range and can independently award funds with the usual bronze, silver or gold rating. Corr is keen to stress that the introduction of machine learning has not led to it employing fewer fund analysts but instead is aimed at helping the firm grapple with the dramatic growth of the fund universe and the persistent presence of orphaned funds with smaller assets and high fees who might have previ- ously not been analysed.
AI and ESG Another field where AI has already been actively implemented is in ESG investing. Dutch pension giant APG, which manages €399bn (£339bn), was among the first pension schemes to apply AI in this field. Since 2016, it uses its tech subsidiary Entis to scan 10,000 listed companies for their compliance with the United Nations’ Sustainable Development Goals (SDG). It has since been joined by €234bn (£199bn) pension fund PGGM. Both funds plan to launch a sustainable development investment asset owner platform by the end of March. In the UK, Brunel Pension Partnership, a local government pool for £30bn worth of investments from 10 partner funds, appointed AI firm TrueValue Labs in October to screen its listed investments for ESG and reputational risks. For Faith Ward, Brunel’s chief responsible investment officer, using AI data provided by TrueValue adds an element of objec- tivity. “TruValue Labs’ data isn’t dependent upon what compa- nies publish about themselves. Their timely material ESG data helps us to continually monitor the managers in our client partners funds and to evaluate and select new managers,” she says.
Peak human?
The factors which drove GPIF to consider using AI in its fund research might not be entirely unfamiliar to many UK funds. As a relatively young fund, launched in 2006, GPIF faced the effects of the first wave of quantitative easing in Japan. The yield compression was painful its portfolio, which was then heavily reliant on fixed income and forced it to shift its money into riskier assets.
Humans are ultimately very expensive fleshy robots. John Beckett
This is indicative of a global trend. Between 2004 and 2014, global assets under management doubled from $37.3trn (£28.6trn) to $78trn (£60trn) and could reach as high as $112trn (£860trn) by the end of 2020, PwC predicts. This growth is linked to the impact of quantitative easing world- wide, with $4.5trn (£3.4trn) in assets bought by the US Federal Reserve. Simultaneously, the growth of passive investments throughout that period shone a light not just on the inefficien- cies of active managers, but also on those working to select
36 | portfolio institutional February 2020 | issue 90
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