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Expanding The Smart Beta Filter: Does It Help?

Summary iShares factor ETFs provide a source of well tested algorithms for factor-based stock selection. Previous examination of QUAL, MTUM and USMV have shown that this approach can produce actionable investing ideas. Can adding other, well-documented, factors improve the selective powers of this approach?. I continue to think about mining the iShares smart beta ETFs for investing ideas. In this article, I want to discuss expanding the source of data to include ETFs for risk premium factors beyond those I looked at previously. Let me start by reviewing some recent results from this exercise. My starting premise is that the set of ETFs offered by Blackrock iShares emphasizing individual risk-premium factors provides a rich source of securities that have passed their quantitative filters for the target factors. Previously I looked at three of these ETF focused on low volatility, quality and momentum factors. My goal was to find stocks that appeared in the holdings from more than one of these ETFs with the idea that such stocks have passed the MSCI index screen for more than one factor. I identified 14 stocks that occur in all three ( A Quest for the Smartest Beta ) and 60 more that occur in at least two ( Can We Find Smarter Beta From 2 Factor Portfolios? ). I found the results intriguing. First, The ETFs all beat the market, as represented by the SPDR S&P 500 Trust ETF ( SPY), as does the equal-weighted portfolio of ETFs. By analyzing a hypothetical portfolio, I was able to show that the 14 holdings from the set occurring in all three ETFs has soundly beaten all of the ETFs as well as the equal-weighted portfolio of the ETFs. This is fully documented in the second article referenced in the previous paragraph. Readers commented on my omission of two of the classic risk-premium factors and offered suggestions on incorporating them into the models. The missing factors, value and size, are, of course, important, and I’m going to look at how much, if anything, they add to the exercise as I go on. But first, let me digress here for a paragraph or two and consider why I felt these factors could, or should, be left out. Let’s start with the objective: It is to mine the quantitative algorithms of MSCI’s factor indexes for high-potential stocks. As I explained in the second article, I wanted to keep this exercise to a manageable number of funds and holdings. I thought three was optimal. Also, value and size are much less straightforward to deal with in this context. These factors form the basis for the traditional classifications of stocks: Value vs Growth and Large-, Mid-, Small-Cap. It’s the Morningstar style box. Value is variously defined and it’s not at all unusual to see the same securities turning up in growth and value funds from the same group. Size is easy, but pairs poorly with other factors depending on how one makes size cuts. By contrast, quality, momentum and low volatility are less rigorously defined (even considering the vagueness of how value is defined) and, in my view, more amenable to quantitative analysis that can produce unique, actionable results. So, I went with quality, momentum and low volatility. Quality is something I’ve been thinking about a lot, and I like the algorithm QUAL is using to define the factor (discussed here ). Momentum is another factor that can add serious alpha. I’ve been maintaining some momentum-based investing strategies in moderate-size portfolios using commission-free ETFs for several years to modest success. A problem with momentum is it tend to generate volatility and I’ve tried to modulate that in my own investing by adding a weighting for volatility (some day I may write an article on this). This reflects my appreciation for low volatility and the thinking that led me to include USMV in this project. The Factor ETFs All this is a bit subjective and intuitive, which is always something to guard against in an evidence-based approach, so I’ve decided to take readers’ advice and look at two more of iShares MSCI factor-index funds. I wanted to see if adding value and size to the analyses can improve the results. To this end, I’ll be deconstructing five ETFs looking for common holdings. The list of five, starting with the three considered earlier: iShares MSCI USA Minimum Volatility ETF (NYSEARCA: USMV ), iShares MSCI USA Momentum Factor ETF (NYSEARCA: MTUM ) iShares MSCI USA Quality Factor ETF (NYSEARCA: QUAL ) iShares MSCI USA Value Factor ETF (NYSEARCA: VLUE ) iShares MSCI USA Size Factor ETF (NYSEARCA: SIZE ) One problem right off the bat is the size of SIZE. At 636 holdings, it’s nearly four times the size of the next largest fund (USMV with 165). Perhaps as a consequence, it adds little value to the analysis, although, despite having 636 holdings, it is the least correlated with the broader market of the five ETFs. (click to enlarge) Pay particular attention to the last column in that table. SIZE is the least correlated with SPY, much lower than I would have anticipated. Note too, that VLUE is less correlated with SPY than any of the other three ETFs. I’ll start by looking at the performance of these ETFs and ask if the two new additions look likely to add any value. (click to enlarge) For the past year, they have lagged the previously considered three. But this has not been a good year for value stocks, and SIZE may add an advantage from that low correlation coefficient that will only become evident when it becomes an important variable. Deconstruction the ETF Portfolios As I did previously, I downloaded the full holdings of each of the ETFs into a spreadsheet and analyzed all five for stocks that appeared in more than one of the funds. Here’s a summary of the results. As anticipated, it quickly gets unwieldy. Only a single stock is in all five funds, and there are 20 that appear in four of the ETFs. Beyond that, there are too many to be useful for my purposes. What’s interesting is the 14 stocks that formed the basis of the earlier analysis by occurring the holdings of QUAL, MTUM and USMV, are all included in the 21 four- or five-fund stocks here. Thirteen of the 14 occur in either VLUE or SIZE; only one is in both. So, if we take the top 22 stocks here, i.e. those occurring in at least four funds, we have added eight to the previous list. So far, so good, we have increase our candidate pool; but not excessively, it’s still a manageable number. Here, for the record, are the 22 stocks with the 14 from MQLV set in italics: Axis Capital Holdings Ltd (NYSE: AXS ), Accenture Plc (NYSE: ACN ), Ace Ltd (NYSE: ACE ), Arch Capital Group Ltd (NASDAQ: ACGL ), Assurant Inc (NYSE: AIZ ), AT&T Inc (NYSE: T ), Chevron Corp (NYSE: CVX ), Chipotle Mexican Grill Inc (NYSE: CMG ), Chubb Corp (NYSE: CB ), Eli Lilly (NYSE: LLY ), Home Depot Inc (NYSE: HD ), Nike Inc Class B (NYSE: NKE ), O’Reilly Automotive Inc (NASDAQ: ORLY ), Partnerre Ltd (NYSE: PRE ), Reynolds American Inc (NYSE: RAI ), Sigma Aldrich Corp (NASDAQ: SIAL ), Starbucks Corp (NASDAQ: SBUX ), Target Corp (NYSE: TGT ), Travelers Companies Inc (NYSE: TRV ), United Health Group Inc (NYSE: UNH ), Visa Inc Class A (NYSE: V ), WR Berkley Corp (NYSE: WRB ). The first entry, Axis Capital, is the single name in all five ETFs. Sector representation is dominated by Financials and Consumer Discretionary, but it is more diverse than the set of 14 derived from three ETFs. (click to enlarge) Here is how these 22 stocks are allocated among the ETFs. As we see, all are in SIZE. SIZE is therefore acting as a binary filter to select among funds that are in three of the four funds but do not pass the size-factor filter. This is potentially a useful filter. USMV holds all but one, so it’s a similar filter. VLUE is a stronger filter. Less than half the funds are in VLUE’s holdings. I find this interesting and would have expected a result like this from MTUM, which only misses four, none of which is likely to be mistaken for a momentum stock in the current market. As I refine my thinking on this whole exercise, I have to spend more time considering how VLUE affects results. Portfolio Analysis As previously, I wanted to see the results of this filtering process. There is only one record to analyze. The funds rebalance at the end of May and November and, to my knowledge, do not publish past index allocations. Thus, there is only one analyzable record, that for the current cycle which is about 5 months old. We can see how various permutations of these results have fared since the last rebalance. I ran analyses on Portfolio Visualizer for equal-weighted portfolios comprising the following with the coding I’ve used in the tables: Five ETFs: 5ETFs EW QUAL, USMV, MTUM: 3ETFs (QVM) EW Stocks present in holdings of at least 4 of the ETFs: VQMVS(4+) VQMVS(4+) stocks in QUAL and MTUM only: QxM VQMVS(4+) stocks in QUAL and VLUE only: QxV VQMVS(4+) stocks in MTUM and VLUE only: MxV I pulled out the last three sets because USMV and SIZE were doing little more than serving as a final filter for the other three ETF holdings’ overlaps, so I thought it useful to see how those components were contributing to the results. Here are those results. (click to enlarge) As we can see, the five ETFs as an equal-weighted portfolio beat SPY, but lagged the subset of three ETFs. Let’s not forget, however, that this is only a five-month result. Longer term results can show benefit to holding all five factor ETFs, or at least four of them. For this we do have a longer record. The full record is still limited as the youngest fund only dates to July 2013. From July 2013, equal-weighted portfolios, rebalanced semiannually, of combinations of five, four and three of the ETFs turned in the following performance results. (click to enlarge) Removing either SIZE or VLUE added return and reduced maximum drawdown. Removing both, i.e. going to only QUAL, MTUM and USMV, as previously considered, improved both metrics. Volatility did increase slightly, but in all cases it remained lower than the S&P 500. These results indicate that there has been no advantage to adding VLUE or SIZE to a factor-based ETF portfolio. I’d like to say this validates my decision to use only MTUM, QUAL and USMV in my analyses, but the fact remains that the data set is too limited to draw such a conclusion. Let’s return to the previous table – and our main topic – and see how stocks filtered from the ETFs on the basis of their presence in four or more funds fared. Over the past five months, the combined ETFs returned 1.40% CAGR for all five, and 5.75% CAGR for the MQLV three. A portfolio of the 22 stocks found in four or more ETFS 29.67% CAGR and did so with a max drawdown of only -3.35% vs. -6.52% for the better performing of the two ETF portfolios. Separating out the component ETFs we see that the combination of QUAL an MTUM added a remarkable level of value, far outpacing a combination of either of the two factors with value as represented by VLUE. Yet again, I must emphasize the limited data available. But the results certainly begin to suggest that these ETFs, especially MTUM, QUAL and USMV, are attractive sources for filtered lists of stocks that rank strongly for risk-premium factors which can be further filtered for having been selected by the quite different quantitative criteria by multiple funds.

What To Do When Your Stocks And Bonds Portfolio Reaches Minimum Volatility

Summary Investors typically increase exposure to bonds as they near retirement, hoping to reduce volatility and drawdown risk. It is very possible to reach a point where further increasing exposure to bonds will increase rather than decrease volatility. This phenomenon is more likely to occur with longer duration bond funds. Once you reach minimum volatility for a two-fund stocks and bonds portfolio, you can further reduce risk by (1) buying treasuries or (2) switching to a shorter term bond fund. There is no general result for which strategy is preferred, but (2) tends to give better returns and may be easier to implement. Expected Returns and Volatility as you Increase Bond Exposure Suppose you are implementing a basic stocks and bonds portfolio comprised of two Vanguard mutual funds: Vanguard 500 Index Fund Investor Shares (MUTF: VFINX ) and Vanguard Long-Term Bond Index Fund (MUTF: VBLTX ). Using historical data going back to Feb. 28, 1994, here is how expected returns and volatility of the VFINX/VBLTX portfolio vary with asset allocation. (click to enlarge) Here the top-right point represents 100% VFINX/0% VBLTX; the next data point is 90% VFINX/10% VBLTX; and so on until the bottom-most point, which is 0% VFINX/100% VBLTX. As you near retirement, you may increase your VBLTX allocation to reduce risk. If you go from 90% VFINX/10% VBLTX to 60% VFINX/40% VBLTX, for example, you reduce your expected returns a little (0.041% to 0.037%), while reducing volatility considerably (1.06% to 0.70%). Further increasing the VBLTX allocation reduces volatility, but only to a point. At 25.8% VFINX/74.2% VBLTX, you reach the leftmost point on the curve, and further increasing VBLTX allocation actually increases volatility while reducing expected returns. Of course, there is never a good reason to increase volatility and decrease expected returns. So looking back at the past 21.5 years, you would never have wanted to allocate more than 74.2% to VBLTX in a VFINX/VBLTX portfolio. Longer Duration Bond Funds Have Lower Critical Points The expected returns vs. volatility curve doesn’t always have a clear critical point like we saw for VFINX/VBLTX. In general, longer duration bond funds are more likely to exhibit this phenomenon. You can see this when you compare the curve for VFINX paired with VBLTX to VFINX paired with Vanguard’s short-term and intermediate-term bond funds, VBISX and VBIIX . (click to enlarge) Looking at the blue curve, VFINX/VBISX does have a minimum volatility point, but it’s at a very high VBISX allocation (4.3% VFINX/95.7% VBISX). Note however that if you’re using VFINX and VBISX you probably wouldn’t want to go higher than 90% VBISX, as doing so sacrifices considerable expected returns while reducing volatility very little (if at all). The green curve is in between the first two, with minimum volatility at 12.7% VFINX/87.3% VBIIX. I would not recommend going any higher than 80% VBIIX, though, from an expected returns/volatility standpoint. Reducing Volatility Beyond the Critical Point What do you do if you want to further reduce volatility after reaching your portfolio’s critical point? I see two reasonable options: Allocate some of your portfolio to treasuries (e.g. 10-year US treasury bonds). Swap for a shorter duration bond fund. Let’s go back to the first two-fund portfolio, VFINX/VBLTX. Suppose we’re at 25.8% VFINX/74.2% VBLTX and we recognize that we’ve reached minimum volatility. We would like to reduce volatility to one-fourth that of VFINX (the leftmost dotted line in the previous figures, at 0.298). We can’t do it with all of our assets allocated to VFINX or VBLTX. Let’s consider option (1). Allocating some of your portfolio to cash would pull the red curve down and to the left. But if you’re going to have cash, you may as well get some interest on it. So instead of cash let’s say we generate risk-free returns on whatever percentage we pull out of our VFINX/VBLTX portfolio, from investing those assets in US treasuries for example. The next figure shows the expected returns vs. volatility curves for various allocations to a risk-free investment that returns 1.5% annually. (click to enlarge) To clarify, the highest curve the same as we saw before; the next highest is 10% receiving risk-free 1.5% annual returns, and the remaining 90% split to VFINX/VBLTX in 10% increments; and so on until the lowest curve (which you can barely see), which is 90% risk-free 1.5% annual returns, and the remaining 10% split to VFINX/VBLTX in 10% increments. The first curve to extend to a volatility of 0.298 is the one with 40% allocated to the risk-free investment. For this portfolio, we would have to allocate the remaining 60% of our assets to 30% VFINX/70% VBLTX, to achieve an expected return of 0.0226% with volatility of 0.298%. Now let’s consider option (2). The next figure is the same as the last one, but with the curves for VFINX/VBIIX and VFINX/VBISX included. (click to enlarge) Interestingly, swapping VBLTX for VBISX lets us reach a volatility of 0.298 with a mean daily return slightly higher than that reached with VFINX/VBLTX and 40% risk-free. A 24.7% VFINX/75.3% VBISX portfolio has means returns of 0.0232%. A natural question is how the risk-free rate affects whether strategy (1) or (2) is better. For the Vanguard funds examined here, strategy (1) would always outperform strategy (2) if the risk-free rate was 4% or higher (i.e. rarely or never). Strategy (2) would always outperform strategy (1) if the risk-free rate was 0% (i.e. you held cash rather than treasuries). For risk-free rates between 0% and 4%, it really depends on the particular level of volatility you’re trying to achieve. Conclusions I think a lot of investors operate under the assumption that increasing exposure to bonds reduces volatility. But in fact there is often a point where further increasing exposure to bonds increases volatility and reduces expected returns. You don’t want to go past that point. To reduce volatility further than your two-fund portfolio allows, you can either allocate some of your assets to a risk-free investment, say US treasuries, or you can switch to a shorter duration bond fund. I favor the second strategy, as it tends to allow for greater expected returns and seems logistically easier to implement. More generally, I think it is very important to know where your portfolio is at in terms of the expected returns vs. volatility curve. You should have a good idea of how any potential change in asset allocation or choice of funds affects your portfolio’s characteristics.