As investors try to diversify an investment portfolio, many have turned to smart-beta exchange traded funds that track alternative indices to potentially generate enhanced returns.
“Many investors are moving away from traditional market-cap indices to smart beta in search of better returns and lower costs,” Alexander Channing, Director of Quantitative Cross Asset Index Strategies at ETF Securities, said on the recent webcast, The Evolution of Smart Beta 2.0.
U.S.-listed ETFs have gathered over $2 trillion in assets under management and smart-beta funds now make up about 20.1% of the U.S. ETF assets.
Channing argues that the shift away from market-cap indices to smart beta may be due to the potential shortcomings in traditional beta indices. Specifically, cap-weighted indices are not designed to maximize risk-adjusted performance, tilt toward unrewarded factors and have low level of diversification. Market cap-weighted indexing methodologies would overweight the biggest stocks that have gathered the most investment dollars, potentially exposing investors to overbought segments of the market.
As a solution to the market-cap weighting methodology, smart beta 1.0 indices came out and focused on one issue at a time. These single factor tilts include value or growth. Additionally, some like the equal-weight methodology, tried to improve diversification.
Now, there are number of more sophisticated products in what Channing calls smart-beta “2.0” indices, which aim to provide a comprehensive approach by addressing both factors and multi-weighting strategies in one index.
When identifying the appropriate factors, Channing pointed to the four common investment characteristics including low volatility, small size, momentum and value. These four factors make up a “multi-factor” approach to potentially lead to better risk-adjusted returns.
“Individual factors can be cyclical and their returns not entirely correlated,” Channing said. “Combining them may potentially lead to better diversification.”
Additionally, to maximize diversification, there are a number of stock weighting strategies. For instance, Maximum De-concentration refers to equally weighting components. Maximum De-correlation weights components according to contribution to overall portfolio correlation. Efficient Minimum Volatility would weight holdings in order to minimize volatility. Efficient Maximum Sharpe Ratio tries to achieve the maximum possible risk-adjusted portfolio returns. Lastly, Diversified Risk Parity refers to weighting components based on the proportion to the inverse of their volatility, so the least volatile stocks have higher weights.
“Individual factors can be cyclical and their returns not entirely correlated,” Mike Cameron, Head of Institutional Sales at ETF Securities, said. “Combining them may potentially lead to better diversification and limit market timing.”
For those interested in investing in these multi-factor strategies, ETF Securities has partnered with ERI Scientific Beta on the relatively new ETFS Diversified-Factor U.S. Large Cap Index Fund (NYSEArca: SBUS) and ETFS Diversified-Factor Developed Europe Index Fund (NYSEArca: SBEU). Scientific Beta is an index provider specializing in smart beta solutions and is part of the EDHEC Risk Institute, an entity that works closely with institutions to implement academic research and improve their investment and risk management process has also recently came out with smart-beta ETFs of its own.
However, potential investors should be aware that these factors and weighting methodologies do not completely remove the risk of investment losses. Instead, these smart-beta indexing methodologies help diminish swings during periods of heightened volatility and reduce falls in times of extreme selling.
Financial advisors who are interested in learning more about the smart-beta ETF strategy can listen to the webcast here on demand.