Since the emergence of “Modern Portfolio Theory” and the “Capital Asset Pricing Model” in the late 1960s, institutional investors have taken a quantitatively driven approach to portfolio construction, looking to create portfolio diversification and obtain better risk-adjusted returns by balancing their asset-class exposures. This journey has seen several important advancements in thinking about how to optimally achieve desired results.
Funds that want to take the total portfolio approach first need to get the total portfolio view. To do that they not only need data – and lots of it – but a rock-solid understanding of exactly how they’re going to use it.
AMP Capital joins data analytics revolution
AMP Capital, a big funds manager, has enjoined the asset servicing sector of the industry in seeking out opportunities for enhanced returns and better efficiencies through data analytics. Perhaps ironically, it’s the chair of the hedge fund group AIMA Australia – Alistair Rew – who is leading AMP’s charge in the field.
Rew normally tends not to speak publicly, notwithstanding his role as chair of AIMA Australia, but he was extensively quoted last week in a client newsletter from AMP Capital under the label ‘Investech Strategies’. The report was written by respected journalist Sean Aylmer. The report tantalisingly explains why AMP invested in an online “gamer”. Rew also emphasises, though, that the human element in data analytics, particularly at the early and the oversight stages, will remain critical.
He is quoted as saying: “While algorithms and computer scientists are now part of the lexicon, Rew says the human side of the data equation is just as important as the computing and analytics. He talks about the talent in the team as though it’s a portfolio; some members have a “feel for the data”, some are highly technical, while others are strong in communication – with each skillset playing a deliberate, important role. It jars slightly with common-held beliefs around the sector.
Data analysis, artificial intelligence and machine learning are all terms bandied around to describe a rules-based system, based on ‘if-then’ scenarios. In effect, the systems allow investors to codify logical decisions, making information processing more efficient and consistent. Logical decision making isn’t supposed to involve sensory perceptions.
“There’s a heavy need within the industry for behavioural psychology,” Rew explains. “We need to know how people make decisions so we can analyse how things are done. We can do all this great work, but it doesn’t mean much if people aren’t then using it to refine what they do.”
So, what’s more important? The data or the behaviours? “We could have wonderful behavioural change, but if we haven’t changed in the right direction, then that’s not going to help,” Rew says. “We can have wonderful data, technology, analytics and visualisation. But, if we’re not seeing behavioural change, then it’s not going to help. I’m a big believer in both the analytical and human sides, in combination, not in competition.”
Behaviour versus data, humans versus machines, are part of an ongoing battle that we’re seeing across the industry, Aylmer writes. What we should be talking about is using data, technology and analytics alongside the power and expertise of people to deliver better collective outcomes. There are areas where technology is better and areas where humans are better. The mix will vary from team to team, strategy to strategy, but we should take advantage of that and build a better overall, integrated team. Rew says: “For example. computers are much better at cleansing data and doing large calculations at speed and high frequency. They can free up analysts to analyse output rather than spending hours cleaning the data. However, as we go through this evolution, we need to make sure we don’t disempower or undermine people and keep them engaged, focusing on where they can add their highest value.”
– G.B.
