Investors that try to use smart beta need to know when to use it, how to use it in their overall portfolios, and how much they should have in different smart-beta products with multiple factors, Deborah Fuhr, London-based managing partner at research house ETFGI, said at AIWeek's ETF Summit on Tuesday.
Speaking on the same panel, Adeline Tan, head of advisory at consulting firm Mercer Hong Kong, highlighted two misconceptions among some of her institutional clients.
On the one hand, a client wanting smart beta might understand the process as one of simply making a decision on the factors and criteria to be employed and selecting the most relevant securities for the ETF.
But that is “not how it works”, she said.
On the other hand, clients are expecting to find factors likely to beat markets across all points in the cycle, which is unrealistic, Tan added.
It takes “a couple of weeks' discussion to explain that it is not [just] about creating an index, [that] behind that there is a lot of consulting and active management,” Tan said, noting how smart-beta ETFs are built and created in a similar way to which an active manager runs money.
Smart-beta investing is a passive form of investment but what sets it apart in broad terms is that the indices tracked are not from the mainstream; they try to capture investment factors or market inefficiencies in a rules-based way in order to deliver risk-adjusted returns that beat traditional market benchmarks.
That said, even index providers have yet to agree on a precise definition of what smart beta is, Fuhr said.
“Sometimes people say a smart-beta index is anything that is not market cap-[based], while other times people say it can be market capped [as long as the index has other characteristics such as] if it it’s currency hedged. So really no agreement,” Fuhr said.
For all this confusion, smart-beta investing is gaining interest among major asset owners in Asia.
Taiwan’s Bureau of Labor Funds is one early adopter and as of March ran six smart-beta strategies: global fundamental, global low volatility, global high dividend, global high quality, Asia-Pacific mixed index, and global sovereign credit. They accounted for about 35% of its NT$1.15 trillion ($38 billion) in foreign mandates.
Australia's Future Fund and Japan's Government Pension Investment Fund are also delving deeper into the use of factor tilts and smart beta with the aim of replicating more cheaply the returns of their active equity managers.
On the sidelines of the ETF summit, one attendee from an Asian reinsurance company told AsianInvestor that his company had yet to invest in smart-beta ETFs but is very interested in setting up such a capability. As a result, it is gathering intelligence around the subject and studying different ways to formulate smart-beta strategies.
Fuhr said that she favours a definition of smart beta in which investors look at managing risk for returns, primarily looking at factors supported by academic research that show how they outperform market-cap indices over long time periods.
It's a view supported by Zhan Xintong Eunice, an assistant professor of finance at Erasmus University Rotterdam in the Netherlands. In a presentation at the summit, Zhan explained her team's findings that many actively managed funds cannot truly generate alpha. Instead, what her study shows is that the performance of many of these funds is a function of the markets they are exposed to.
That’s where smart-beta strategies can play a key role – by generating alpha through factor investing, Zhan said.
Her team’s research suggests the mutual fund industry will probably go down two separate ways in the future: by either providing smart-beta ETFs with lower management fees or by striving to generate alpha in a way that was more akin to the hedge fund industry.
From an index provider’s perspective, there are three possible ways ETF products can be broadened out in the future, said John Davies, global head of exchange traded products at S&P Dow Jones Indices, at another presentation at the summit.
Firstly, by providing access to new sources of returns or risk factors across various asset classes such as fixed income ETFs. Secondly, through differentiated multi-factor and multi-asset approaches to create well-diversified or dynamic allocation products, and lastly via the application of non-traditional data (artificial intelligence, big data, etc.) in alternative or specialised asset classes.
The first two directions can be linked to smart-beta strategies.