Confidence in factor investing is growing as the available strategies become more sophisticated, but investors must be aware of unintended risks and biases within the process.
Dimensional Australia and Asia-Pacific portfolio management head Bhanu Singh said the key to factor, or systematic, investing in both passive and active portfolios is identifying the differences between particular returns and how prices are determined.
“At the end of the day, investors care about the cash flows they receive and the prices they pay,” he said, speaking at the 2017 Conexus Financial Equities Summit. “That’s been true for 2000 years.”
Those constructing portfolios should be careful not to introduce “noise” when adding more than one signal to their modelling, he explained, adding that “nuances around implementation” are important to avoid factor friction, which could detract from a portfolio’s performance.
The longest available data set from the US market, stretching back 90 years, is now being studied by some “smart and very motivated” people looking for patterns that would provide reliable premiums and risk going forward, Singh said.
“Unfortunately, what happens a lot of time is people start with the data, find a pattern and invent a reason why the pattern should exist,” he said. “There’s a plethora of strategies with very nice-looking simulations but it’s very difficult to know their premiums going forward.”
Unsurprisingly, a company’s profit today remains a reasonable indicator of future profitability, particularly if it’s higher than a competitor’s.
“If a company is highly profitable today relative to its peers, there’s a high probability it will be, against its peers, tomorrow,” Singh said.
Portfolio builders could combine low-valuation and high-profitability factors for example, in an intelligent way, and “do better than just holding them in isolation”.
Australian uptake mixed
Joining Singh on stage was Sarkis Tepeli, a senior consultant with Frontier Advisors, who said factor investing had been a mainstream strategy for about five years but its adoption by Australian clients was mixed.
Many super funds began by introducing low-volatility factor strategies, reflecting their desire to limit drawdowns during stressed market environments, he said.
Like Singh, Tepeli said it is imperative for investment chiefs implementing factor-based strategies of any kind to think about the specific product design and manage their exposure to unintended factors.
If you combine two naïve or poorly designed products, such as those with exposure to value and quality, they could wash each other out, he said.
“You want to maximise your exposure to your intended factors while minimising any biases, he explained.
Another “strange” problem Tepeli pointed to was the risk of sector concentration for FANG stocks – Facebook, Alphabet, Netflix and Google (now owned by Alphabet) – which are included “by almost every” factor product at the moment.
Investors seeking exposure to quality, profitability, growth, momentum and low volatility could end up with an unintended bias towards these types of stocks, he warned.
HESTA Super listed assets investment manager Dominique D’Avrincourt said the $41 billion industry fund’s portfolio included factor strategies within its global equities portfolio focused on value, quality, momentum, and low volatility.
This was driven by scale, cost-efficiency, and an objective to keep turnover and trading costs down without giving up too much in returns.
“HESTA is a growing fund and scale benefits are critical,” D’Avrincourt said. “We cannot afford to manage 30 to 40 active strategies; there has to be an element of factor strategy in there that gives us scale in investing in them.’’
About 25 – 30 per cent of HESTA’s portfolio is invested via passive strategies, with the rest deployed in a combination of active and factor strategies.
D’Avrincourt said HESTA had avoided using “naïve, off-the-shelf” factor strategies strategies, such as MSCI’s value and minimum volatility indices, because they had unintended risk attached to them and did not account for value traps or companies that were cheap for a reason.
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