A decade ago the impact of passive investing was just beginning to be felt. It is now an unavoidable component of investors’ toolkits. Echoing passive’s exceptional growth, many experts now believe a new trend is emerging, known as core investing.

“It’s an alternative to passive,” says Simon Weinberger, managing director and portfolio manager at BlackRock. “It’s by no means clear that we will continue to have such a strong run in markets, and I think clients will learn to appreciate the consistency of alpha. If you think about retirement, you want to think as much about the ability to generate excess returns, as well as the risk taken by the manager. This kind of product tends to return 1.5-2% after fees, and if you accumulate this over a longer time horizon, that is absolutely significant.”

Central to core investing and creating alpha is the integration between humans, complex computer algorithms and big data. Weinberger describes the process as a ‘constant feedback loop between human and machine,’ with advances in artificial intelligence allowing for many parts of the investing process which previously took money and time to be automated.

“So automating the discovery of investment strategies in the first place, and automating the combination of various strategies.” Some believe the human side of fund management could also eventually change, with advisers being replaced by intelligent chatbots for many of the one-to-one conversations with clients.

With funds under pressure to finance more technology and data, in future, clients may have to decide whether they want to accept robo-advisers or higher fees. “I think humans still want to interact with other humans in situations where there’s an intense emotional decision to be made, although apparently there’s robots being created which can produce human sensitivity,” says Petronella West, CEO at Investment Quorum.

However, fund managers still insist there will be many important roles for humans in investing going forwards. They argue that while machines can do increasing amounts of the groundwork, humans are still crucial when it comes to making the decisions.

“We’re not at a point where the machines will tell us where to find data or what kind of data is potentially interesting to feed an algorithm,” Weinberger says. “We still need people who have an understanding of markets, and know where to look for the right data that can be used by the machine to extract some value in a systematic fashion. Just like you want somebody with an understanding of economics to in the end understand the portfolio, its exposures, and be able to articulate why, for example, we have an overweight position in a given stock.”

Weinberger points out that if the entire process was left to machines, something as simple as a data provider changing the formatting of their data could break the entire system, and lead to spurious forecasts and decisions. “I think all the way from idea generation to owning the outcome of the machine, you do need a partnership between human and machine,” he says.

THE EXPERIENCE THAT COUNTS

While we have access to more data than ever before, Weinberger argues that because there is considerable noise in markets, it requires an expert understanding of the market environment to put particular trends into perspective and then make an informed decision, based not just on the data but experience.

“You need humans to work with machines when it comes to judging whether something is going in line with expectations or not,” he says. “We do a lot of things where we try to predict revenues, and that is conditional with the assumption that ultimately, the market cares about which companies are going to exceed expectations, which ones are going to disappoint. Now, there are markets where everybody is obsessed by how Brexit is going to play out, and what is it going to mean for online versus high street retailers? So, you might have moments in the market where a company reports to the upside and yet, the stock price actually underperforms. Now that would be an instance where there’s no need to panic, as they’re more or less doing what they’re supposed to do.”

However, with much academic machine learning research focusing on interpretability and computer models being able to explain the reasons behind an apparently unusual suggestion in a complex dataset, a point may well come when humans trust machines enough to partake in the decision making process too.

“This is probably the fastest growing research area,” Graham says. “Over the past couple of years we’ve discovered that humans are actually not as resistant as we thought to trusting a machine. They just need suggestions about why it’s making the decision it’s making, and if we can speed up interpretability, we’ll probably speed up by several orders of magnitude the integration of machine learning in our lives.”

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