For instance, Shirbini pointed to well-accepted academic risk-reward factors like low-volatility, value, momentum and size.
“Individual factors can be cyclical and their returns not entirely correlated,” Shirbini said. “Combining them may potentially lead to better diversification.”
Additionally, a combination of weighting strategies like maximum deconcentration, risk parity, maximum decorrelation, minimum volatility and maximum Sharpe ratio, can also help diversify risk or help limit drawdowns.
“Academia shows a low correlation of parameter estimation errors,” Shirbini said. “Combining weighting strategies reduces the impact of parameter uncertainty.”
ETF Securities has partnered with ERI Scientific Beta on the ETFS Diversified-Factor U.S. Large Cap Index Fund (NYSEArca: SBUS) and ETFS Diversified-Factor Developed Europe Index Fund (NYSEArca: SBEU). The two ETFs’ selection process includes emphasizing investment factors, such as volatility, valuation, momentum and size. Additionally, the ETFS Diversified-Factor U.S. Large Cap Index Fund and ETFS Diversified-Factor Developed Europe Index use a proprietary weighting strategy to provide well diversified exposure, by combining 5 models: Maximum Deconcentration, Maximum Decorrelation, Efficient Minimum Volatility, Efficient Maximum Sharpe Ratio, and Diversified Risk Weighted.
“Individual factors can be cyclical and their returns not entirely correlated,” Mike Cameron, Head of Institutional Sales of ETF Securities, said. “Combining them may potentially lead to better diversification and limit market timing.”
Financial advisors who are interested in learning more about smart-beta ETF strategies can watch the webcast here on demand.