Matthew Bartolini is a vice president at State Street Global Advisors and head of SPDR Americas Research. He manages a team responsible for the product research and analysis of SPDR ETFs, and the development of market outlooks, investment themes and portfolio implementation ideas. We recently sat down with Bartolini, who will be speaking at our 2^nd Annual Smart Beta Strategies Summit (Oct. 26 – Boston) to gather his thoughts on defining what falls under the smart beta umbrella, factor timing, fixed income and The Beatles.

Financial Advisor magazine: In your opinion, how many smart beta factors are there?

Matthew Bartolini: There are five main, broadly accepted smart beta factors— also known as premia or exposures—that research is shown to have historically outperformed the market-cap benchmark over the long term: Value, Size, Low Volatility, Quality and Momentum.

These five factors have been empirically proven to explain the cross-sectional variance of returns.[1] And the advent of smart beta strategies means investors can harness the potential benefits of these premia in a rules-based, transparent and cost effective manner.

Just as Billy Preston is often referred to as the fifth Beatle, dividend yield is commonly viewed as the sixth smart beta factor. The effect of yield, or carry, is not as well documented, but we believe that smart beta is about more than harnessing premia. It is also about approaching specific objectives, in this case income generation, in a rules-based manner. Therefore, while not necessarily a true “factor,” dividend yield has a place in the smart beta discussion—much like Billy does with the Beatles.  

FA: Since 2013, smart beta assets have grown from $200 billion to more than $600 billion. Are we in a bubble? Where do we go from here? Is it more of a marketing gimmick than anything else?

Bartolini: The asset levels quoted are eye catching but are not really 100 percent accurate. The basis for my claim is that three major data providers, Morningstar, FactSet and Bloomberg, each report a different figure for flows, asset levels and the number of ETFs deemed to be “smart beta.” This both complicates analyzing a potential “bubble” and confuses the end investor. If leading data providers cannot agree on how to categorize smart beta, how can we expect investors to understand the category and conduct proper due diligence before implementation?  

For instance, one of the providers classifies DIA, the SPDR Dow Jones Industrial Average ETF, as smart beta. I am hard pressed to think that back in 1884 Charles Dow figured he was constructing a smart beta index when he placed 11 stocks in a table in the Customer’s Afternoon Letter publication. That is not smart beta. Sure, DIA is not market-cap weighted, but what premia does the Dow Jones Industrial Average Index seek to capture by employing a price-weighted methodology? None. Rather, the Index was intended to give investors information about stock activity back in a time when the market was noticeably speculative. Dow literally came up with the average by taking the stocks’ closing prices and dividing by 11!!

Clearly, an agreed upon classification for smart beta strategies and ETFs would benefit the end investor. In fact, we are working on an internal classification of smart beta that is rooted in buying behavior and aligned to factor premia.

One topic of debate is to include sector funds that employ alternative weighting mechanisms. For instance, a sector fund that employs an equal weighted methodology is likely not targeting the size factor, but instead weighting in a non-market cap weighted fashion to potentially get a better representation of a particular market segment. At the very least, sector based alternative weighted strategies deserve their own category, as the buying behavior is less about the factor and more about the sector.

Based on our initial work, smart beta ETF assets under management now approach over $300 billion [2], which, in my opinion, is far from any sort of bubble given the size of global equity markets ($43 trillion) [3] and the fact that not all factor strategies will have the same methodology, and therefore, holdings.

FA: Can factor cyclicality be used to generate alpha?

Bartolini: Successful core factor investing must account for cyclicality of factor performance. Multi-factor approaches may provide diversification benefits and offer the potential for improved consistency in performance over a long-term investment horizon. While not “alpha” per se, as these are premia available in the market, multi-factor strategies can potentially address cyclicality and enhance performance.

With respect to factor timing, the difficulty of timing factors has been well-documented [4], given the uncertainty of exogenous elements (macro, risk, sector, etc.) affecting their behavior and the complexity of the underlying relationships. Some have applied a fundamental evaluation measurement to factor valuations to time when factors are rich or cheap; however, this may lead to another form of value investing cloaked as factor timing. A fundamental view may work for a longer time horizon to tilt the portfolio one way or another; however, timing factors with precision is difficult in the short term. As a result, it is quite difficult to do from a top-down perspective by rotating among single factor exposures (e.g. single factor ETFs).  

Therefore, any sort of timing may be best left to active managers to focus on at the single stock level and apply factor tilts that are dynamically shifted to account for a myriad of the exogenous factors. It likely should be part of the process, but not the process. After all, understanding the interdependencies of macroeconomic and market behavioral influences on factor premia is indeed at the heart of the active quantitative process.

FA: Do any of the factors researchers have found in equities also apply to fixed income? How should advisors explain factors specific to fixed income, like duration and credit risk, to their clients?

Bartolini: For investors, smart beta approaches in fixed income can be an effective bridge between market-cap weighted index exposures and less transparent, higher cost active management.

Fixed income investors naturally position around duration, credit, term and liquidity in their investments. Couple this with the ability to target different duration and credit profiles, and it becomes clear that fixed income may be a natural place to apply smart beta.

FA: Why has it taken so long to develop the factor-based fixed income index and product space? Factor-based fixed income products, on average, have often underperformed the market-based bond indexes in recent years—do you expect that trend to reverse as more and better smart beta products proliferate?

Bartolini: Smart beta ETFs are still in the early innings, and fixed income smart beta is barely out of the first inning. A lot of hesitation remains for investors gravitating to the space, but that is largely a function of time. As with any strategy built on the promise of potentially improved performance, absolute or risk-adjusted, an adequate surveillance period with a live track record provides some degree of confidence.

Relying on a backtest of how these factors may have performed is a start, but today’s investors require more stringent controls within their due diligence process. The current review process feels similar to active manager due diligence, where consistency of performance over a market cycle such as three or five years is needed for full vetting.  

As smart beta due diligence continues to evolve, we will remain at the forefront of providing investors the investment background and perspective they need to analyze, review and implement all types of smart beta strategies.

Financial Advisor magazine: Thanks, Matthew. We look forward to hearing more of your thoughts at the 2^nd Annual Smart Beta Strategies Summit October 26^th in Boston.

 

1. Clifford S. Asness, Andrea Frazzini, and Lasse H. Pedersen, 2013 “Quality Minus Junk”
Mark M. Carhat, 1997, “On Persistence in Mutual Fund Performance”
Eugene F. Fama, Kenneth R. French, 1992, “The Cross-Section of Expected Stock Returns

2. SPDR Americas Research as of 08/31/2017

3. Bloomberg Finance L.P. as of 09/14/2017

4. http://www.institutionalinvestor.com/Article/3670089/asset-management-macro/cliff-asness-blasts-rob-arnott-on-factor-timing.html

 

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The views expressed in this material are the views of Matthew Bartolini and are subject to change based on market and other conditions. Information represented in this piece does not constitute legal, tax, or investment advice. Investors should consult their legal, tax, and financial advisors before making any financial decisions.

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Exp. Date: 9/30/18