‘Smart Beta’ Looks Like Expensive Beta

June 08, 2015

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 features Dan Egan, director of behavioral finance and investing at the New York-based automated investing service Betterment.


With high-fee active managers and hedge funds in decline, and technology lowering much of the cost of day-to-day investment management, how can a fund manager justify a higher-margin product?


Judging by the past, such a product would likely play on consumers’ desire to beat the market, without trading off liquidity, concentration or opacity risks.


Enter “smart” beta funds, the newest in a long line of investment funds offering the possibility (not the guarantee) of higher risk-adjusted returns compared with the market. The investment decisions of these new funds aren’t based on gut feel and the networking ability of a human advisor, they use quantitatively designed algorithms.


They are often marketed based on hypothetical historical data regarding various factor risk premia. They cost up to 600 percent of similar normal-beta ETFs. And they are growing quickly.


But, are they good for investors?


So far, the answer is no.


Market Capitalization: Still The Anchor

The starting point for any allocation model is a market-capitalization-weighted portfolio. By anchoring to market capitalizations, you free-ride on the collective wisdom of millions of investors and traders globally. You also know how much you are diverging from market allocation; that divergence is the foundation for generating outperformance.


The same logic can also inform portfolio construction for clients who want to take on more or less risk. While the market holding of U.S. small-caps, for instance, reflects the risk that investors on average are willing to bear, individual investors may be comfortable with more or less risk in their portfolio. It is up to the individual advisor to determine how to take on more or less risk while maintaining a diversified portfolio.


The cleanest way to do this is to use the global market as a benchmark for determining asset allocation. Allocations should consider the market value of available assets, and the implied expected returns from those assets should guide a proportional allocation to the different markets. This was the insight behind the pioneering work of Fischer Black and Robert Litterman in creating the Black-Litterman model for creating diversified portfolios at every risk level.



Factors Are Explainers Of Variance, Not Predictors Of Value

Researchers seeking to explain fund performance have identified up to 300 factors that can independently explain historical investment risks of a portfolio. In effect, each of these is a dimension that an individual investment is assessed on, like price-to-earnings ratio.


A fund is then assessed in terms of its exposure to these factors, and its performance deviation from a passively managed equal factor portfolio.


While factors help explain why a given fund performed a certain way, factors are not necessarily good predictors of future performance. Nor are they necessarily a compensated risk premium, a key difference discussed in a recent Vanguard white paper.


There are many risks you could take on that don’t have a positive expected risk premia. Lotteries, casinos and currency risk are good examples. Just because they explain variance doesn’t mean they’re attractive.


They’re All Factors, Anyway

So it’s important to understand that a market-cap portfolio is a factor portfolio. It just takes on the market allocations of each factor. Investing in “smart” beta portfolios means the manager overweights some factors and underweights others. Some of these may be static overweights, while others may be dynamic, depending on the market cycle.


However, while the jury is still out on whether actual implementations successfully produce alpha, most evidence implies they don’t.


Implementation Issues

A real investment fund is different from the theoretical ideal of an index. For investments where the precision of managing to the index matters, and the expected alpha of the fund is small, implementation costs can swamp alpha.


Proponents of smart beta claim they are simply applying the teachings of financial economists who have found return predictors other than market beta. While we don’t quibble with their findings, it’s another issue entirely whether those predictors continue to hold in such a way that they can be used to generate outperformance in a fund after fees, taxes and implementation.


Issue 1: Is smart beta just an expensive way of getting high beta?

Consider the below graph that compares a long-running smart-beta fund RSP, which equal-weights stocks in the S&P 500 versus an S&P 500 Index tracker. 



Is it fair to say RSP has outperformed? Its cumulative returns are definitely higher. But its declines are also steeper. By equal-weighting stocks, the fund has overweighted (in a very simple manner) to smaller-cap stocks relative to market cap. This is often a simple way of taking on more risk, with a similar outcome to using leverage.



Once You Control For Risk ...

We’ll take two of the smart-beta ETFs with the longest track records: the Guggenheim S&P 500 Equal Weight ETF (RSP | A-84) and the PowerShares FTSE RAFI US 1000 Portfolio (PRF | A-88). To assess performance of these actual investments, we’ll perform the simplest risk-adjusted test possible—testing for risk-adjusted outperformance after accounting for the risk exposure from a market-weighted investment.


If the smart-beta ETFs are successful at delivering better risk-adjusted returns, they will have a positive alpha coefficient in a regression. If they are just more volatile than the market-cap benchmark, they’ll have a beta greater than 1.


The results depicted below show zero improvement in volatility-adjusted returns, but a beta coefficient (risk-taking) of greater than 1.




Taking on more risk, on average, leads to higher returns. This is hardly outperformance. A consistent finding with smart-beta ETFs is that they take on more risk, not just different kinds of risk.


Their volatilities tend to be greater than their market-cap benchmarks, which must be controlled for when assessing performance. If the consumer could have achieved a similar result without a smart-beta fund by simply increasing risk (and saving higher costs), that would have been a preferable strategy.


But two examples—RSP and PRF—might not convince you.


Instead, take research conducted by Denys Glushkov, research director at Wharton Research Data Services, covering 164 smart-beta ETFs from 2003 to 2014. According to Denys:



I find no evidence that SB [smart beta] ETFs significantly outperform their risk-adjusted passive benchmarks. Positive returns from intended factor bets are offset by negative returns from unintended factor bets resulting in an overall performance wash.


Risk-adjusted performance of SB funds is also insignificant when compared with the performance of the blended benchmark that provides passive cap-weighted exposure to market, size, and value factors. After decomposing benchmark-adjusted performance of SB funds into selection, static, and dynamic allocation effects, I find that their factor timing ability is neutral at best.


Issue 2: Higher (and hidden) costs to the consumer

While the higher expense ratios of these “active” index funds is clear, there are other hidden costs associated with investing in these funds. The first two derive from the higher transaction volume caused by a nonmarket-cap index.


Nonmarket-cap-based indexes have substantially higher transaction volumes because, to generate a chance of outperformance, they need to periodically re-deviate from their previous holdings. They do this both by selling out of some assets and buying into others. This exposes investors to transaction-based costs.


Liquidity Transaction Costs

Every market transaction exposes the fund (and its customers) to transaction costs such as the bid/ask spread. The more active a fund is, the more often it’s exposed to these transaction costs.


Market-cap index funds have built-in, transaction-minimizing features. As prices change, the funds’ holdings automatically match the market-cap-weighted allocations. The turnover required in a market-cap-weighted portfolio derives only from index inclusions and corporate actions, which are less frequent and less expensive.


Many active index funds have higher turnover due to the need to track the index in a way that often moves against market-cap weighting.


Tax Transaction Costs

These transactions (hopefully) will generate taxes in taxable accounts. Like other ’40 Act funds, ETFs pass through both the tax gains and losses to the end investors. As a result, while the pretax returns may beat a passive index, the after-tax returns may be significantly less than a more passively managed strategy in a taxable account.


In the case that the returns do not keep up with the passive strategy, the turnover can continue to reduce the end investor’s returns.



A Negative Risk Premium?

The core principle of all investing is earning a risk premium.

If you’re bearing uncertainty, you should be able to demand to be paid more, especially net of fees and taxes. Smart-beta funds appear to be based on the opposite notion; namely, that the investor should bear known higher costs—management fees, transaction costs and tax costs—in exchange for the very uncertain chance that active indexes will outperform on average and over a long period of time.


And it’s far from certain that documented historical performance by smart-beta factors will persist.


A recent paper by McLean and Pontiff in the Journal of Finance reviewed the persistence of such premiums out-of-sample (not based on backtests) and after publication of the premiums. They find that portfolio returns are nearly 30 percent lower out of sample, and 60 percent lower post-publication. That’s market efficiency reducing the effectiveness of known risk premia.


So, as it was before the growth of smart-beta funds, market-cap-weighted indexes are probably still the worst way to invest, except for all those others.

At the time of writing, Betterment did not own any of the securities mentioned. Betterment is the largest, fastest-growing automated investing service, helping people to better manage, protect, and grow their wealth through smarter technology. It is a CNBC Disruptor 50 and Webby award winner and has been featured in the New York Times, Forbes, and the Wall Street Journal. Learn more here.



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