(Jon Sage)
While smart beta strategies have been attracting a lot of interest from investors, there are other ways to capitalise on quantitative factors in markets with the additional protection afforded by fundamental analysis. A recent paper by MFS Investment Management demonstrates this.
MFS, which is best known in Australia for its flagship fundamental global equities fund, has been managing money since 2011 with a range of “blended” products as well. It has two Australia-domiciled trusts: the Blended Research Global Equity and Blended Research Low Volatility Global Equity trusts.
Jon Sage, portfolio manager of the blended research strategy, visited Australia from Boston last week. He described ‘Blended Research’ as “an affordable alpha seeking strategy for those investors looking for a consistent, risk controlled and differentiated alpha source”.
The strategy has low turnover, averaging 45 per cent annually, a portfolio size of between 80 and 140 stocks – averaging 114 – and fees which are between those of fundamental active and smart beta or enhanced passive (but closer to the latter). More importantly, it is designed to outperform over the longer term through cycles when beta strategies tend to work better and when fundamental active strategies work better.
The white paper includes a study of returns over 21 years to 2014, illustrating the periods when managers who described themselves as quantitative outperformed and periods when managers who described themselves as fundamental outperformed. Over that time, there were two periods where fundamental active outperformed and three periods where the beta-orientated strategies outperformed. Generally, the active managers outperform during higher volatility and down markets and quants when markets are rising.
Sage, who has been with MFS for 15 years, says: “The Blended Research process is not another quantitative strategy. We have seen that quant and fundamental approaches perform differently in different market environments, which is why by systematically combining the two sources we feel we can provide more consistent returns overtime and over different market environments.”
“We combine quantitative and fundamental viewpoints on stocks into a single signal … The portfolio construction process is designed to generate excess return from security selection. Other benchmark risks are neutralised by keeping sector and industry weightings and market-cap distributions similar to the index.”
Quant managers, like traditional active managers, have gone in and out of general favour over the years, because of the behavioural problem investors have in chasing past returns. In 2007, for instance, as cracks started to appear in the global financial system, a lot of quant managers suffered big drops in short-term performance.
Sage says: “I think what we saw in 2007 was that many quantitative managers were dynamically shifting their model factor weights to those that were working so when the crisis hit many quant managers were overweight factors that had suddenly become out of favour.
“Our quantitative models are built to be more strategic and have a longer-term focus relative to other quant models. We weren’t dynamically shifting weights to those factors that were working at the time so we weren’t too exposed to any individual factor. Our blended approach helped us in that environment as we had a fundamental input that was offsetting some strong signals from the quant side.
“The significant shift that we’re seeing into passive is perhaps partly due to softer returns from many active managers, but is also driven by our industry’s ever-growing focus on costs. The Blended Research product with its lower fee relative to other active funds is well positioned in this space. “Furthermore, as the ‘decumulation’ phase moves higher up the agenda and the demand for decumulation-style products increases, product offerings like our blended research low volatility trust are starting to attract more attention, given their more outcome-oriented tendencies.”
The MFS blended strategy uses multi-factor models customized for each sector and region. They seek to identify attractively valued stocks of high quality by evaluating factors with certain investment themes: valuation, earnings quality, earnings momentum, price momentum and the firm’s “indicator model”.
Sage says it has skews towards quality, value and momentum, which the firm sees as alpha factors. “We avoid unintended skews to risk factors that are not alpha factors in our approach, like size for example.”
– Greg Bright