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Summary The smarter-beta strategy uses three smart-beta ETFs as sources for an investable portfolio that provides exposure to three risk-premia factors. The factors are low volatility, momentum and quality. In this article I report on a backtest of the strategy using data from the inception of the youngest of the three ETFs. I started an exercise to mine three of iShares smart-beta ETFs for investment ideas. My idea was to use the portfolios of the funds, which are designed to provide broad exposure to one of the risk-premia factors, as a source for devising and investable portfolio that provides exposure to all three factors. The three ETFs I selected are: iShares MSCI USA Minimum Volatility ETF (NYSEARCA: USMV ) iShares MSCI USA Momentum Factor ETF (NYSEARCA: MTUM ) iShares MSCI USA Quality Factor ETF (NYSEARCA: QUAL ) These are, as the names tell us, focused on low-volatility, momentum and quality factors. I refer you to my first article on the topic where I describe the methods and concepts in detail ( A Quest For The Smartest Beta ). Briefly, I compare the portfolios of the funds and select the equity positions that are held by all three. This is illustrated in the Venn Diagram to the right. I combine the stocks that overlap the portfolio holdings of all three funds in an equal-weighted portfolio. Readers have pointed out that I’m neglecting at least two important factors, value and size, which are also cards in the iShares ETF smart-beta deck. I looked into this ( Expanding The Smart Beta Filter: Does It Help? ) and concluded they offered no advantage over the three I selected. This was based on a very limited data set as I’ll describe, however. With access to earlier cycles for the funds’ portfolios it may be worth the effort to revisit this question as well. One feature of these funds is that their indexes are rebalanced twice annually, on the last business days of May and November. Until today, I was unable to do any sort of backtest. So, when I first introduced the concept in November I used the portfolio that was put into effect in June 2015 and looked at returns over the five-month period. At the end of November, I published a rebalanced portfolio ( Momentum, Quality and Low Volatility: Continuing the Quest for Smarter Beta ) and results for the full six-months of the ETFs’ rebalancing cycle. Those results were highly encouraging. Each time I wrote on the topic, I lamented not having access to historical portfolios for the funds to further explore performance. Then a sharp-eyed reader added a comment pointing out where those data were available (thanks again, ipaul66 ). So, I’ve downloaded holdings data going back to end-of-November rebalance for the inception of QUAL, the youngest of the three funds, in August 2013. I’ve also shown that the three funds together in an equal-weighted portfolio turned in a solid performance record vs. the broader market represented by the S&P 500 TR index (^SPXTR). I’ve included that portfolio in this analysis as a comparison. The backtest covers two years, still woefully short, but a huge improvement on six months. There are four six-month cycles with complete results. The most recent cycle began on the last day of November, so we have nothing meaningful from that as yet. CAGR Let’s start with the big result: CAGRs for each of the strategies. This table shows CAGRs for each six-month cycle for the smarter-beta portfolio (MQLV), the S&P 500 TR index, and the equal-weighted ETFs (3ETFsEqWt). Both the MQLV and the three ETFs beat the S&P 500. Only for the Dec 2013 through May 2014 cycle does the broader market outperform. Commutative and Cycle Returns The next chart shows cumulative return on $100,000 invested in the three strategies on December 1, 2013 through the November 29, 2015. (click to enlarge) And, for $100,000 invested at the beginning of each semi-annual rebalancing cycle: (click to enlarge) Conclusions and Caveats These results do support and validate the earlier finding. The smarter-beta strategy appears to be an effective filter that can add meaningful alpha relative to the broader market, or to equal-weighting the three source ETFs. I caution, however, that this is based on only two years’ history, and for a quarter of that period the smarter-beta strategy sharply under performed. The model is equal-weighted which may not be optimal and weighting needs a closer look. Having this two-year data set will give me the opportunity to explore other weighting strategies. This analysis makes no allowance for trading costs. One can often buy an S&P 500 index fund in a commission-free ETF. The three-ETF portfolio requires at most twice-yearly rebalancings for modest cost. The MQLV portfolios comprised 12 to 19 positions over the two years, so trading costs are significant, especially for smaller portfolios. If I introduce a 0.25% slippage factor (which allows for trading costs but not spread costs) the CAGR falls to 15.46% for a $100,000 portfolio, still beating the S&P 500 handily, but it does illustrate the cost of turnover. For a smaller portfolio, a larger slippage factor is required. For a $10K initial investment, 32 annual trades at $8/trade would be 2.56% and that much friction drops the CAGR to $10.17%. Even assuming the best interpretation of these results, the strategy generates substantial turnover and is only suitable for reasonably large portfolios (or for those who have accounts that provide free trades). I mention this because I have had commenters suggest they might try the strategy with only a small number of shares for each position. For the investor who is not interested in the turnover and trading this strategy will require, the equal-weighted portfolio of the three ETFs is an attractive alternative. That strategy did not turn in a single negative cycle, more than can be said for either the smart-beta portfolio or the S&P 500. Trading costs are modest with a maximum of 12 trades a year for the semi-annual rebalance, but even that may not be necessary as the ETFs do not vary much from on another over the course of a year or two. Comparing the two-year CAGR of 11.68% to 9.58% for the broad market would seem to indicate that the strategies being used in the MSCI indexes do in fact capture alpha from exposure to the risk-premia factors. Scalper1 News
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