2015 has been good to smart beta, gaining $63 billion in inflows and earning recognition as ‘buzzword of the year’ on Investopedia; Smart Beta has come to dominate new ETF products’ AUM growth and marketing dollars. Whether you call them ‘Strategic Beta’ or ‘Enhanced Beta ETFs’, these strategies claim to fame is their professed ability to deliver alpha at lower fees than active funds while still outperforming “dumb-beta” passive strategies.
Smart Beta strategies offer investors the best of both worlds: transparency through a rules-based approach, low expense ratios, and the opportunity to access alpha. The target audience is defined: the active portion of the index portfolio. The target competition is defined: active managers who run closet beta portfolios. And the recipe is simple: employ factor screens to select and weigh underlying stocks to provide tilts that have historically outperformed market-cap weighted indices and are expected to continue to do so due to inefficiencies in the market. Which immediately begs the question: Is this outperformance for real, and will it continue?
I’ll start out with a disclaimer: I do believe in factors. I believe in factors that have been vetted and debated on extensive out-of-sample data, including international settings, over decades. What makes the smart beta products difficult to evaluate for investors is the emergence of hundreds of new or rebranded factors for which actual track record is limited. While all historical simulations look great, they are no guarantee of future performance. Looking only at the top journals, the number of factors “discovered” grew from 1 per year in 1990 to 18 per year in the last decade. This seems highly suspect as the low-hanging fruit of true factors was most likely picked early.
Additionally, in earlier decades when the cost of data-mining was high, only factors based on economically sound principles were tried and tested. (Read: the factor model concept may now have as much or more marketing prowess as investment promise).
Not only are these factors confusing for investors, it remains unclear if the excess returns can be attributed to the strategies. In a Journal of Portfolio Management study published in 2013, the authors looked at smart beta factor strategies such as minimum volatility, value, or fundamentals- weighted, etc. They inverted the signals from these sensible factor-based rules and expected to see the upside-down portfolios perform poorly. However, they discovered that reversing the factors, for example, using high volatility or inverse of fundamentals to weigh stocks, also outperformed the S&P 500! Paradoxically, using Malkiel’s blindfolded monkey approach, throwing darts at the Wall Street Journal, produced portfolios that did better than the market.
The authors believe that these results exemplify that almost all these strategies have an unavoidable tilt to value and small caps, and therefore, show superior back-tests. There might be additional biases at play as explained in an NBER working paper released this summer. The paper stated that if you take 3 or 4 random signals with no real predictive power and combine them into a strategy, you will always find a strong back-tested performance. This bias and false outperformance from picking the best 3 out of 10 random strategies are as bad as data-mining the best performing signal from approximately 1000 choices. This worsens if you then weigh the signals by how strong they are, rather than using equal weight.
These biases are evident in recent research published by Astor, wherein we compared a few smart beta indices’ after they went “live”. We discovered that, on average, the alpha decreases while its beta (the return from market risk) increases, demonstrating how out-of-sample “smart” beta may have more “dumb” beta than you think.
Does this mean all smart beta strategies are inherently flawed? Not at all! Factors have been used by quants and active money managers for decades and true factors have proven to work in different economic environments, as well as internationally. However, to avoid getting lost in the factor marketplace, here are some rules of thumb:
- Evaluate the Factors: Other than empirical testing, is there a credible reason for this factor premium to exist? Is it highly researched? Has it exhibited gain over a long history?
- Watch your Back (test): Compare the volatility, beta, and alpha of the historical-simulated index before it went live versus the ETF’s performance.
- Check for Data-Mining: Especially for composite smart-beta strategies that combine several factors. Go under the hood and evaluate the signal individually. Determine if they have predictive power on their own or if they have been data-mined from hundreds of possible options
- Have Stricter Thresholds: Some studies recommend using a high threshold Sharpe ratio of 3.0 or higher to adjust for biases.