Unlike traditional size and style strategies, there is no clear and concise definition for smart beta.
On the SPDR Americas Research team, we take five steps to identify and classify smart beta strategies.
Our smart beta classification schema and other resources can be valuable tools in the due diligence process as smart beta ETFs proliferate.
Over the last 10 years, smart beta ETFs have been among the most popular strategies to launch as investors have become more familiar with the idea of factor investing. Or rather, investors have become more familiar with the notion of harnessing well-documented premia (such as value and momentum) in search of returns in excess of broad-market beta. During this time, smart beta assets have ballooned by 2,371%, growing from $14 billion to $357 billion.1 Moreover, the asset mix has moved away from traditional dividend strategies toward more factor-based approaches, with the share of smart beta ETF assets held by dividend funds falling from 59% to 44%.2
This shift in buying behavior indicates investors are increasingly viewing their portfolios through a factor lens. They are choosing among smart beta funds that provide specific exposure to a range of factors, including minimum volatility and size, or opting for multi-factor funds that offer exposure to multiple factors. Lately, fund launches have become more sophisticated, with many multi-factor blends and quantitatively-driven (e.g., optimized) products coming to market. The chart below illustrates these trends.
As smart beta ETFs garner more attention and become more sophisticated, more in-depth due diligence is required. In our view, this begins with defining the selection universe, an opaque topic in the field of smart beta investing. Here we explore the current challenges of smart beta classification and illustrate how our team approaches the subject.
Before we tackle classifications, it’s helpful to level-set by stating a few central beliefs about smart beta strategies. It is commonly held that:
The smart beta classification conundrum
Most smart beta classifications adopt either a selective “I’ll know it when I see it,” or a throw-it-all-in-there “kitchen sink” mentality. As a result, unlike traditional size and style strategies, there is no clear and concise definition for smart beta. The lack of consensus around what constitutes smart beta results in strategies with seemingly similar names—but in reality, very different construction methodologies.
For example, let’s consider “value” funds as identified by FactSet. At year-end 2018, there were 29 value funds focused on large- or broad-market US equities. The return dispersion between those 29 strategies in 2018 was 12.5%! The best-returning fund was down 4.05% while the worst declined 16.54%. From a risk perspective, the volatility of these strategies ranged from 10% to more than 15%. The chart below illustrates this risk and return dispersion among strategies sharing the same “value” moniker. The dispersion looks like someone just broke the rack in a game of pool, not a breakdown of similar strategies.
Furthermore, the average number of holdings in the funds was 302, ranging from as few as 21 holdings to as many as 1,322.3 The vastness of this range can catch investors off guard unless they carefully conduct in-depth due diligence, much like they would when choosing an actively managed fund. A checklist—such as the one we recently created—can help investors look beyond the label and ensure they’re asking the right questions. All due diligence, however, starts with identifying the applicable universe, and therefore starts with classifications. The old adage of “know what you own” definitely applies here.
Step-by-step: How we approach smart beta classification
Creating classification schemas is a difficult task. There is no right or wrong answer, and therein lies the main problem with smart beta investing. If major data providers—and for that matter, fund providers—cannot agree on what constitutes a smart beta strategy, how will investors be able to fully implement it as part of their investment process? Some include a fund tracking the price-weighted Dow Jones Industrial Average as smart beta, which feels a bit off. It’s unlikely Charles Dow was the innovator of smart beta strategies back in 1884 when he first put together a table of 11 stocks in the Customer's Afternoon Letter, a daily two-page financial news bulletin which was the precursor to The Wall Street Journal.
The chart below depicts the central issue of seemingly random walk down smart beta street. It illustrates the total number of smart beta ETFs and their assets as identified by three major data providers and according to the SPDR Americas Research definition. Given that each data provider has a different definition, we see different figures for assets and number of funds. Traditional style exposures are included by data providers, which we exclude. But that is not the only wrinkle we apply.
*Per SPDR Americas Research
Source: Bloomberg Finance L.P., Morningstar, FactSet, as of 12/31/2018. Calculations by SPDR Americas Research.
On the SPDR Americas Research team, we take five steps to identify and classify smart beta strategies:
Our aim: Provide clarity in a nebulous field
The last chart below is a recreation of the earlier chart, but this time with the sub-asset classes (sector, dividend) broken out. By creating different classification tiers for smart beta and being transparent about our process, we aim to provide clarity in a nebulous field that features an abundance of different strategy types—all carrying the same name. As more smart beta strategies come to market, our classification schema and other resources can be valuable tools in the due diligence process, helping investors go from identification to implementation.
*Per SPDR Americas Research
Source: Bloomberg Finance L.P., Morningstar, FactSet as of 12/31/2018. Calculations by SPDR Americas Research.
For our latest smart beta insights, you can follow SPDR® Blog or visit the smart beta section of our website.
1 Bloomberg Finance L.P., Morningstar, as of 12/31/2018. Calculations by SPDR Americas Research.
2 Bloomberg Finance L.P., Morningstar, as of 12/31/2018. Calculations by SPDR Americas Research.
3 Bloomberg Finance L.P., FactSet, as of 12/31/2018. Calculations by SPDR Americas Research.
Measures the volatility of a security or portfolio in relation to the market, usually as measured by the S&P 500 Index. A beta of 1 indicates the security will move with the market. A beta of 1.3 means the security is expected to be 30% more volatile than the market, while a beta of 0.8 means the security is expected to be 20% less volatile than the market.
The views expressed in this material are the views of SPDR ETFs and State Street Global Advisors Funds Research Team and are subject to change based on market and other conditions. This document contains certain statements that may be deemed forward-looking statements. Please note that any such statements are not guarantees of any
future performance and actual results or developments may differ materially from those projected.
Value stocks can perform differently from the market as a whole. They can remain undervalued by the market for long periods of time.
A “quality” style of investing emphasizes companies with high returns, stable earnings, and low financial leverage. This style of investing is subject to the risk that the past performance of these companies does not continue or that the returns on “quality” equity securities are less than returns on other styles of investing or the overall stock market.
A Smart Beta strategy does not seek to replicate the performance of a specified cap-weighted index and as such may underperform such an index. The factors to which a Smart Beta strategy seeks to deliver exposure may themselves undergo cyclical performance. As such, a Smart Beta strategy may underperform the market or other Smart Beta strategies exposed to similar or other targeted factors. In fact, we believe that factor premia accrue over the long term (5-10 years), and investors must keep that long time horizon in mind when investing.
Low volatility funds can exhibit relative low volatility and excess returns compared to the Index over the long term; both portfolio investments and returns may differ from those of the Index. The fund may not experience lower volatility or provide returns in excess of the Index and may provide lower returns in periods of a rapidly rising market. Active stock selection may lead to added risk in exchange for the potential outperformance relative to the Index.