Returns from thematic robo-advice portfolios can be boosted by using quantitative metrics that won't increase investors' risk, according to digital investment solutions company Quantifeed.
In its whitepaper, Designing thematic indices with a quantitative factor, investors with robo-advice portfolios can improve their risk-return profiles by combining traditional stock selection, building indices and overlaying this with quantitative factors.
Quantifeed senior quantitative strategist Gaudi Schneider said: "Mass affluent consumers in Asia Pacific who are receiving wealth management online can truly benefit from a quantitative overlay to their portfolios."
"Financial institutions, on the other hand, can also capture the opportunity of digital wealth management services not only by servicing this segment of clients remotely, but also by delivering to them an enhanced portfolio performance," he added.
Schneider said there are two distinct approaches to building indices: thematic indices, based on investment themes; and factor indices, based on empirical research of past investment returns.
While the former approach is based on a forward-looking growth story for a niche industry, the latter uses quantitative variables, such as volatility, yield, size and momentum.
Schneider said one way of introducing a quantitative factor to a given thematic portfolio is to apply weights to securities based on a specific variable, for example, volatility, instead of the standard weights based on market capitalisation.
"Low volatility stocks have been shown to outperform higher volatility stocks over extended periods of time. Taking advantage of this phenomenon can be achieved by giving stocks with low volatility a greater weight in the portfolio. We call this inverse volatility weighted."
Quantifeed looked at a group of US-listed companies active in designing robots and automation services and applied the inverse volatility weighting quantitative factor. Its model outperformed the original index by 11 percentage points over a three-year period between February 2014 and 2017.