Tag Archives: consumer

Can We Find Smarter Beta From 2 Factor Portfolios?

Summary Smart beta ETFs offer a rich source of data for factor-based investing. I use ETF focused on low volatility, momentum and quality factors as sources for mining these data. Here, I look at the stocks that share positions in two of the ETFs with an objective of identifying stocks that rank positively for two of the factors. Smarter Beta? Maybe. In the first article in this series ( A Quest for the Smartest Beta ), I dissected three Blackrock iShares smart beta ETFs. Each of these is designed to exploit a single risk-premium factor: low volatility, momentum or quality. The three ETFs 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 ) As we saw a portfolio equal-weighting the three ETFs handily beats the broader market as represented by the SPDR S&P 500 Trust ETF ( SPY), and can provide an excellent entry into factor-based investing. In that first article went on to look at the full portfolios for the three funds and determined which of the holdings overlapped more than one fund. Fourteen of a total of 336 equity positions are currently held by all three. I analyzed those 14 as an equal-weighted portfolio I called MQLV. That analysis formed the focus of the first article. This is the Venn diagram showing the full overlap for the three funds’ holdings. (click to enlarge) As we saw previously the MQLV portfolio, comprising the 14 positions shown in the box on the right of the figure, turned in an exceptionally strong performance since its inception in June. The obvious follow-up question is to ask about the two-factor overlaps. How well have they performed and how do they compare to one another? As you can see these are interesting complexes of securities. Let’s begin by consider what is in each of the three clusters. The LVxQ cluster is the largest, containing 32 positions. This is not a complete surprise as quality and low volatility do tend to track together. Quality, as defined for the purposes of QUAL’s index includes three fundamental variables: Return on Equity, Debt to Equity and Earnings Variability. I discussed QUAL and its index in detail previous ( here ). It’s not unreasonable to expect that stocks from companies that rank highly for those metrics would also exhibit lower volatility. The 32 LVxQ stocks are: Apple Inc (NASDAQ: AAPL ), Ace Ltd (NYSE: ACE ), Automatic Data Processing Inc (NASDAQ: ADP ), Berkshire Hathaway Inc Class B (BRKB), Costco Wholesale Corp (NASDAQ: COST ), Campbell Soup (NYSE: CPB ), Chevron Corp (NYSE: CVX ), Dollar Tree Inc (NASDAQ: DLTR ), Henry Schein Inc (NASDAQ: HSIC ), Hershey Foods (NYSE: HSY ), Intuit Inc (NASDAQ: INTU ), Gartner Inc. (NYSE: IT ), Johnson & Johnson (NYSE: JNJ ), Lockheed Martin Corp (NYSE: LMT ), Mastercard Inc Class A (NYSE: MA ), Mcdonalds Corp (NYSE: MCD ), Marsh & Mclennan Inc (NYSE: MMC ), 3M Co (NYSE: MMM ), Monsanto (NYSE: MON ), Microsoft Corp (NASDAQ: MSFT ), Paychex Inc (NASDAQ: PAYX ), Public Storage Reit (NYSE: PSA ), Qualcomm Inc (NASDAQ: QCOM ), Ross Stores Inc (NASDAQ: ROST ), Sherwin Williams (NYSE: SHW ), At&T Inc (NYSE: T ), l TJX Inc (NYSE: TJX ), Travelers Companies Inc (NYSE: TRV ), Varian Medical Systems Inc (NYSE: VAR ), VF Corp (NYSE: VFC ), Exxon Mobil Corp (NYSE: XOM ), and Yum Brands Inc (NYSE: YUM ). Sector allocations are led by Information Technology, Consumer Discretionary and Financials. (click to enlarge) The MxLV cluster holds 21 positions. These are: Allergan (NYSE: AGN ), Autozone Inc (NYSE: AZO ), C R Bard Inc (NYSE: BCR ), Church And Dwight Inc (NYSE: CHD ), Dollar General Corp (NYSE: DG ), Ebay Inc (NASDAQ: EBAY ), Facebook Class A Inc (NASDAQ: FB ), Fiserv Inc (NASDAQ: FISV ), General Mills Inc (NYSE: GIS ), Alphabet Inc Class C (NASDAQ: GOOG ), Alphabet Inc Class A (NASDAQ: GOOGL ), Mondelez International Inc Class A (NASDAQ: MDLZ ), Mccormick & Co Non-Voting Inc (NYSE: MKC ), Partnerre Ltd (NYSE: PRE ), Synopsys Inc (NASDAQ: SNPS ), Stericycle Inc (NASDAQ: SRCL ), Target Corp (NYSE: TGT ), UDR Inc. (NYSE: UDR ), Unitedhealth Group Inc (NYSE: UNH ), Vantiv Inc Class A (NYSE: VNTV ), Water Corp Corp (NYSE: WAT ). Sector allocations are led by Information Technology and Consumer Staples. (click to enlarge) The QxM cluster with seven positions is the smallest. I find it interesting that momentum correlates poorly with quality using the definitions of these ETFs. With the 14 stocks included in the 3-ETF overlap cluster, there are only 21 stocks that meet the index criteria for both quality and momentum. The seven stocks in this cluster are: Assurant Inc (NYSE: AIZ ), Brown Forman Corp Class B (NYSE: BF.B ), CDK Global Inc (NASDAQ: CDK ), Edwards Lifesciences Corp (NYSE: EW ), Progressive Corp (NYSE: PGR ), SEI Investments (NASDAQ: SEIC ), Torchmark Corp (NYSE: TMK ). More than half (4 of 7 positions) of the sector allocation for this cluster is to financials. Consider that financials was a dominant sector in the MQLV cluster as well, where it accounts for four of the 14 positions. (click to enlarge) Portfolio Performances What happens when we try to create portfolios from each of the 3 clusters? Ideally, we’d have the data to track changes as the ETFs indexes rebalanced. But I’m unaware of any publicly available sources for historical portfolio compositions for either the ETFs or the Indexes. So we’re restricted to current holdings. Each of the three indexes are rebalanced semi-annually at the end of May and the end of November. The current clusters have been in place since the last rebalancings implemented on June 1. What I’ll do is compare how equal weighted portfolios for each of the clusters compare in performance and risk metrics since June 1. My plan is to come back to this at the end of this month and see how the portfolios have changed. My expectation is that USMV will have changed the least, closely followed by QUAL. MTUM will have changed the most; such is the nature of momentum-it’s transient. I’ve used Portfolio Analyzer to track portfolio performances for the 14 positions in each of the 3 ETFs and SPY for reference standards. The results are quite interesting. (click to enlarge) MQLV is the clear standout here. It is followed by QxM and MxLV. The third two-factor cluster (LVxQ) underperforms everything but SPY. This tends to suggest that momentum was the key factor for this five-month period. But, let’s look at the ETFs. Each beats SPY but none stands out as having been exceptionally better than the other two. QUAL is the best performer of the three but only by a slim margin, and USMV is the worst, but again only by a slim margin. The previous indication that momentum was the key to performance over this time span is not borne out by the full portfolio performance records. I suspect an important driver for these results is the size of the portfolios. The smaller portfolios are more highly selected for the factors under consideration. The 32 position LVxQ portfolio comprises some excellent holdings, many of which I have in my own portfolio. But a critical look at that list makes clear that this is not a group of stocks one would target for short-term outperformance. I don’t own the ones I do for that purpose and I doubt many do. Another driver is the stability of the portfolios. I expect that LVxQ will be the most stable of the four. As I said, momentum is transient and it is the momentum factor that is going to most strongly affect how the various models change at rebalancing. Obviously, what we have here is a single data point. It is impossible to draw any conclusions from these results. But the fact remains that they are intriguing and suggest that this approach may have merit in pulling out attractive opportunities for stock picking on a semi-annual basis. Investors with longer term perspective can use the LVxQ cluster as a resource for portfolio constructions. Those more willing to trade regularly may be more attracted to the MQLV group, but they should be prepared to rebalance, perhaps extensively, at 6 month intervals. I will certainly be interesting to see what the month-end restructuring of the indexes brings. I’ll be on it and I’ll try to get a report out here as quickly as I can get it done. Before closing it should add that there are many other ETFs one can choose from to exploit the various risk-premia factors that have been identified. I’ve selected these three because I’m familiar with them (I hold all three), I considered that their approaches complemented rather than duplicated one another, and because I’ve found that iShares and MCSI, the index provider for these funds, tends to provide accessible and transparent data for my research. It also helps that they all have the same sources because starting with the data all in the same format makes for much more efficient use of my time. As readers commented, I’ve not included two of the best-documented factors: value and size. This was an intentional choice. Size was excluded because it made more sense to me to restrict myself to large- to mid-caps. That was an easy call. Excluding value was less obvious. I wanted to limit the analysis to three funds which I think is the sweet spot for this sort of thing. More than three gets unwieldy. I felt these three factors — low volatility, momentum and quality — had minimal overlap but two of the three had some overlap with value. I also felt adding value as a factor would have de-emphasized momentum to a greater extent than I wanted. I have no real evidence for this point of view, but it made intuitive sense to me. As it happens, one value factor counterpart of these funds, the iShares MSCI USA Value Factor ETF (NYSEARCA: VLUE ), has 21 positions in common with MTUM, as many as USMV. Regardless, I did not want to replace either QUAL or USMV with VLUE. Might be grist for another go-round however.

Valuation Dashboard: Financials – November 2015

Summary 4 key factors are reported across industries in the Financial sector. They give a valuation status of industries relative to their history. They give a reference for picking stocks in each industry. This article is part of a series giving a valuation dashboard by sector of companies in the S&P 500 index (NYSEARCA: SPY ). I follow up a certain number of fundamental factors for every sector, and compare them to historical averages. This article goes down to the industry level in the GICS classification. It covers Financials. The choice of the fundamental ratios has been justified here and here . You can find in this article numbers that may be useful in a top-down approach. There is no analysis of individual stocks. A link to a list of individual stocks to consider is provided at the end. Methodology Four industry factors calculated by portfolio123 are extracted from the database: Price/Earnings (P/E), Price to sales (P/S), Price to free cash flow (P/FCF), Return on Equity (ROE). They are compared with their own historical averages “Avg”. The difference is measured in percentage for valuation ratios and in absolute for ROE, and named “D-xxx” if xxx is the factor’s name (for example D-P/E for price/earnings). The industry factors are proprietary data from the platform. The calculation aims at eliminating extreme values and size biases, which is necessary when going out of a large cap universe. These factors are not representative of capital-weighted indices. They are useful as reference values for picking stocks in an industry, not for ETF investors. Industry valuation table on 11/4/2015 The next table reports the 4 industry factors. For each factor, the next “Avg” column gives its average between January 1999 and October 2015, taken as an arbitrary reference of fair valuation. The next “D-xxx” column is the difference as explained above. So there are 3 columns for each ratio. P/E Avg D- P/E P/S Avg D- P/S P/FCF Avg D- P/FCF ROE Avg D-ROE Commercial Banks 15.42 15.24 -1.18% 2.97 2.06 -44.17% 19.79 13.44 -47.25% 8.78 8.89 -0.11 Thrifts & Mortgage Finance* 18.66 20.66 9.68% 2.97 2.03 -46.31% 21.55 14.75 -46.10% 6.25 5.02 1.23 Diversified Financial Services 21.45 17.85 -20.17% 4.36 2.94 -48.30% 19.78 16.13 -22.63% 8.04 6.38 1.66 Consumer Finance* 11.58 13.15 11.94% 1.64 1.47 -11.56% 6.68 8.22 18.73% 13.36 11.83 1.53 Capital Markets* 16.39 18.07 9.30% 3.58 3.06 -16.99% 19.55 19.62 0.36% 8.96 7.89 1.07 Insurance 14.24 13.7 -3.94% 1.29 1.07 -20.56% 10.77 8.99 -19.80% 9.31 8.71 0.6 REITs** 35.85 35.42 -1.21% 5.36 4.56 -17.54% 49.26 38.74 -27.16% 5.24 4.07 1.17 Real Estate Management** 30.22 31.19 3.11% 3.79 3.06 -23.86% 24.68 25.55 3.41% 4.27 -1.33 5.6 * Averages since 2003 – ** Averages since 2006 Valuation The following charts give an idea of the current status of industries relative to their historical average. In all cases, the higher the better. Price/Earnings: Price/Sales: Price/Free Cash Flow: Quality (ROE) Relative Momentum The next chart compares the price action of the SPDR Select Sector ETF (NYSEARCA: XLF ) with SPY (chart from freestockcharts.com). (click to enlarge) Conclusion XLF and SPY have distinct ways but very similar returns in the last 6 months. From the valuation charts above, we can note that some industries look overpriced, but all of them are above or close to their historical averages in quality. Two industries in the sector look more attractive than others: Consumer Finance and Real Estate Management & Development. For both of them, 2 valuation factors out of 3 and the quality factor are better than their respective averages. Commercial Banks, Diversified Financial Services, Insurance and REITs are overpriced for the 3 valuation ratios. Commercial Banks look the weakest industry of this study, with all metrics in negative territory. However, there may be quality stocks at a reasonable price in any industry. To check them out, you can compare individual fundamental factors to the industry factors provided in the table. As an example, a list of stocks in Financials beating their industry factors is provided on this page . If you want to stay informed of my updates, click the “Follow” tab at the top of this article. You can choose the “real-time” option if you want to be instantly notified.

The European Local Recovery: Introducing A New Index

By Jeremy Schwartz Earlier, we discussed how positive trends in the European economy showing domestic growth are leading the eurozone , while global trade has been one of the weak points. 1 We also discussed how our favorite leading indicators of the economy-both M1 growth and the European Commission’s Economic Sentiment Indicator-were showing positive signs that bode well for future trends in the local economy. 2 What could be a good way to position toward this local economic recovery? Creating an Index to Respond Strongly as Economic Conditions Improve At WisdomTree, we build innovative equity Indexes that offer the opportunity to express certain characteristics or have greater potential to respond to different economic trends. If an economic recovery in Europe is truly taking hold, we wanted to create an Index that best reflects these local economic conditions. WisdomTree thus created the WisdomTree Europe Local Recovery Index to reflect attributes of an improving domestic economy that is less reliant on the global export markets. Especially over the past five years, certain more defensive sectors of the MSCI EMU Index have exhibited lower correlation to changes in the economy and the leading indicator of activity, the European Commission’s Economic Sentiment Indicator. These defensive sectors thus may not offer the most representative exposures to improving economic conditions within the eurozone. Over the past five years, those same defensive sectors have exhibited lower betas when measured against the returns of the MSCI EMU Index. In times of turmoil or uncertainty, this could be a potentially positive attribute, but if an investor truly believes in the prospects for a eurozone economic recovery, these lower-beta defensive sectors are likely to be least responsive to a more positive growth environment. Defensive Sectors Less Correlated to Changes in Economic Activity and Sentiment (click to enlarge) Positioning in Cyclicals: No Defensives In positioning for local economy recovery, these data points lead us toward a preference for cyclical sectors over defensive sectors. Within the WisdomTree Europe Local Recovery Index, the Consumer Staples, Health Care, Telecommunication Services and Utilities sectors are not eligible for inclusion. Two important factors are driving allocations in the WisdomTree Europe Local Recovery Index: Stock Selection: In addition to the aforementioned sector screens, there is also a geographic revenue requirement to ensure a domestic European focus: constituents must derive more than 50% of their revenue from inside Europe, giving focus to what is happening within Europe and less sensitivity to the global growth outlook. Weighting: We also employ a weighting methodology to maximize sensitivity to improving economic conditions. This process tilts the weight toward stocks whose returns have been most correlated to changes in economic conditions, defined by the European Commission’s Economic Sentiment Indicator discussed above. This unique weighting methodology ranks stocks by their correlation to the Economic Sentiment Indicator and, using a smoothed weighting process, tilts weight from the traditional benchmark market capitalization weights toward stocks that are more responsive to changes in economic sentiment and activity. Formally, the weights are set by two factors: 25% according to their market capitalization percentages, and 75% according to how correlated each stock is to economic activity over the last five years (based on each stock’s returns and its relationship to the European Commission’s Economic Sentiment Indicator). Bottom Line 3 : Local Focus: WisdomTree Europe Local Recovery Index has nearly 70% of its weighted average revenue coming from within Europe. Opposite of WisdomTree Europe Hedged Equity Index: This is a distinctly complementary approach to that employed by the WisdomTree Europe Hedged Equity Index, which requires constituents to derive more than 50% of their revenue from outside Europe. The weighted average revenue exposure from Europe in that Index is only 30%. Unhedged Local Exposure Complements Hedged Exporters: There has been a huge amount of interest in currency-hedged eurozone exporters in 2015. The unhedged local recovery basket provides a nice complement both from its unhedged nature and the distinctly different profile of stocks represented in the local recovery Index. Based on the macroeconomic trends discussed in our blog post ” A Recovering Eurozone Economy: Where Should You Position? ,” this local recovery index should also be a focal point for traditional unhedged replacements, as the local economy is showing relative strength within the European economy. Sources Bloomberg, Eurostat and WisdomTree, with data as of 6/30/15. Bloomberg, European Commission, European Central Bank and WisdomTree, with data as of 9/30/15. Bloomberg, FactSet, with data as of 9/30/15. Important Risks Related to this Article Investments focused in Europe increase the impact of events and developments associated with the region, which can adversely affect performance. Jeremy Schwartz, Director of Research As WisdomTree’s Director of Research, Jeremy Schwartz offers timely ideas and timeless wisdom on a bi-monthly basis. Prior to joining WisdomTree, Jeremy was Professor Jeremy Siegel’s head research assistant and helped with the research and writing of Stocks for the Long Run and The Future for Investors. He is also the co-author of the Financial Analysts Journal paper “What Happened to the Original Stocks in the S&P 500?” and the Wall Street Journal article “The Great American Bond Bubble.”