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Limit Risks and Diversify with Multi-Factor, Smart-Beta ETFs

Exchange traded funds (ETFs) that implement smart-beta strategies help investors move away from traditional market-cap methodologies to generate better returns without paying for the high costs associated with an active manager.

On a recent webcast, How Smart Beta is Getting Smarter and Why Advisors Should Pay Attention , Eric Shirbini, Global Product Specialist at ERI Scientific Beta, highlighted some shortcomings associated with market cap-weighted index funds, including a tilt toward unrewarded factors and low level of diversification, which may lead to less desirable risk-adjusted returns.

Consequently, fund providers have worked with indexers to create smart-beta indices to address the problems. In the beginning, we saw so-called smart-beta 1.0 solutions, or strategies with a single factor tilt, like value or equal weight. Now, the industry has come out with smart-beta "2.0" indices that utilize factors based on well established empirically rewarded factors and multi-weighting strategies that weight components to maximize diversification.

"Many rewarded factors are under-represented in client portfolios," Joe Smith, Senior Market Strategist at, CLS Investments, said.

For instance, on the webcast, 63% of polled advisors are not allocated toward smart-beta strategies. However, the majority of advisors, 56%, say they are adding smart-beta ETF exposure over the next six months. About 27% of surveyed advisors, though, require more information, which reflects the ongoing need to educate investors about alternative index-based strategies.

Smith advised investors to focus on long-term, rewarded sources of risk while minimizing exposure to unrewarded risks as a way to steer toward broad and consistent drivers of superior returns.

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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 .

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

This article was provided by our partner Tom Lydon of etftrends.com.


The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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