4 Rules To Evaluate Smart Beta ETFs

June 23, 2016

This article is part of a regular series of thought leadership pieces from some of the more influential ETF strategists in the money management industry. Today's article is by Ben Lavine, chief investment officer of 3D Asset Management based in East Hartford, Connecticut.

I was recently speaking with an advisor who made some pointed observations about our ETF managed portfolios. He noted that several of our ETF holdings lack even a one-year track record and/or hold less than $100 million in assets under management.

He also voiced concerns about some ETFs not trading in sufficient size or volume, or even on a frequent basis. He more or less affirmed that, based on his own criteria for evaluating traditional mutual fund strategies, several of our ETFs would not even make it past his screens.

He couldn’t understand how we can be comfortable holding such ETFs.

These are, of course, valid concerns, but they come from a traditional approach to evaluating active managers. Should an advisor use the same screening criteria to evaluate ETFs, particularly strategic-beta ETFs?

Replacing Active Management With Factors

3D Asset Management has a long history of investing in strategic beta. Our approach seeks to replicate smart investor behavior at a fraction of the cost of active management. In the spirit of Fama/French, we believe that much of what an active manager delivers can be systematically replicated using factor portfolios mimicking active managers’ styles and decision rules.

But how do we pick strategic-beta ETFs? We focus on four main criteria:

  • Design
  • Implementation
  • Expenses
  • ETF sponsor reputational strength and commitment to supporting the ETF

According to Morningstar, there are about 400 U.S.-listed equity strategic-beta ETFs designated as “return oriented” or factor-based portfolios, with some $398 billion in AUM. Of these, 308 (or 77%) have a one-year track record, 227 (or 57%) have a three-year track record and 184 (or 46%) have a five-year track record. 

 

 

In addition, 209 (or 51%) have $100 million or less in AUM, and 133 (or 33%) have $25 million or less. In fact, despite the growth in AUM, the top 20 ETFs (by AUM) comprise nearly two-thirds of AUM within this category, suggesting there is an opportunity for the strategic-beta space to broaden out as more ETF strategies gain traction. 

 

 

Advisors using traditional evaluation screens would most likely gravitate to the larger, more established ETF strategies. This would serve as the “safer” approach, but would leave them unexposed to much of the product innovation happening within strategic beta.

A Hypothetical Case Study: A Faux S&P 500 ETF

Consider this extreme, exaggerated hypothetical example to illustrate why a traditional screening criterion applied to mutual funds should not apply to ETFs:

Imagine, if you will, the 3D Faux S&P 500 ETF: This hypothetical ETF will be designed to capture the wisdom of the crowds based on the intuitive rationale that the collective wisdom is superior to any one individual opinion.

Basket securities will be weighted based on equity market capitalization, reflecting the accumulated value creation assigned by the market. It will focus on the largest 500 stocks by market capitalization and will be reconstituted twice a year.

This will be an index-tracking, passive ETF. For basket construction, we will use a stratified sampling approach that will not fully replicate the tracking index, but will minimize tracking risk by targeting index characteristics and risk exposures.

We will seed it with $10 million from a liquidity provider to get the fund launched, and the fees will be competitive to other similar strategies.

Finally, we have reasonable assurance that the 30+ liquidity providers will maintain active markets for Faux S&P and will compete on the bid/offer spread and will be incentivized to ensure the ETF price does not deviate too far from the net asset value.

 

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