Tag Archives: management

Stock Screening And EV/EBIT

Summary This begins a series of articles communicating my investment philosophy, strategy, and process. I’ve always used stock screens. They’re basically a necessity and more people use them than those just using explicit screening tools. What you screen for matters, and I like to use EV/EBIT for reasons explained below. Screening and using rules like EV/EBIT are fundamental to adding a passive, systematic layer to my investment process, which I feel complements the deep research and intuition. As a new investment manager, I’d like to begin communicating my investment philosophy and strategy in a coherent way. A series of articles will be part of the process of communicating my process. In this, the first article, I will focus on how I source ideas and why I do it this way. Screening I begin with a stock screen. A stock screen is a query of the universe of public securities. There are tens of thousands of public companies globally. There’s too many for one person to do detailed research over any reasonable period (say 1 market cycle or 5-10 years). Unfortunately, I’m one person, so screening it is. There are some gifted investors who manage to be more than one person and who avoid screens. Warren Buffett famously went through every company in the Moody’s stock manuals from A-Z in his partnership days. Others who avoid explicitly using screens and don’t go A-Z rely on intuition to find companies worth analyzing. For example, perhaps an investor reads every new idea published on Value Investors Club or Seeking Alpha, two popular investment research sites I use later in my process. I’d argue this is still screening, just using non-financial criteria. These investors are screening for securities based on the criteria that they are covered on VIC or SA. Criteria are the heart of stock screening and I think they’re a necessity. Evolution What criteria do I use? Since I began investing several years ago, I’ve tried many different criteria. I began with pre-made screens like the Piotroski F-Score, Magic Formula, Ben Graham’s Net-Nets, and others. Gradually I moved toward making my own screens with tools like finviz.com . After a few years, I moved in a different direction. I’d done a lot of reading about the underperformance of most active managers and behavioral psychology. I’d begun playing poker and thinking about the future more in terms of odds and possibilities than certain outcomes. These and other factors led me to use stock screening for more than just finding stocks with metrics that I intuitively like. I began wanting the screens to return a basket of stocks that, based on extensive historical data, would outperform on average. I became interested in systematic strategies. Helpful books on this path were: Joel Greenblatt’s The Little Book that Beats the Market , which I’ve read several times Tobias Carlisle and Wesley Grey’s Quantitative Value James O’Shaughnessy’s What Works on Wall Street Investing on the long side is not zero sum. Stocks in the US have gone up just under 10% nominally and just under 7% really since the 1800s. But as an active investor, I am implicitly not content with market performance. I am trying to achieve what most active investors covet: long-term, sustained market outperformance. Alpha. When you think of market performance as “zero,” the market is zero sum. From there, it is a good idea to frame the question not as “How do I invest?” but instead “How do I sustainably outperform?” Base Rates I think a big part of the answer is by selecting stocks from baskets that outperform. Surely among the unmanageable tens of thousands of stocks out there, there are many baskets of a few hundred, selected based on various criteria, that have historically outperformed. Indeed there are. The academics are all over this. Here I will highlight and contrast just two though. Momentum One is momentum. This is buying stocks that have increased recently and either selling them when they begin to decline (“trend following”) or just holding them for a designated period like one year. This takes many forms because there are many definitions of “increased recently.” Has it increased in the last minute, hour, day, week, month, 50 days, 200 days, year, or 5 years? In general, I’ve gathered that over periods of measurement less than a year, momentum predicts outperformance. Once you extend it further to 5 years, this actually reverses. Momentum then underperforms and stocks that have performed the worst over the prior 5 years (“dumpster diving”) outperform. Here is some data from What Works on Wall Street to support the claim that momentum has predictive value. I won’t elaborate on the details of the tests but they did seem substantive and compelling: Strategy (from universe of All Stocks) Geometric Average Return 1951-2003 All Stocks 13.00% 50 Best 1 year price performance (“1YPP”) 12.61% 50 Worst 1YPP 4.06% Strategy (from universe of Large Stocks) Geometric Average Return 1951-2003 Large Stocks 11.71% 50 Best 1YPP 14.73% 50 Worst 1YPP 9.11% Strategy (from universe of All Stocks) Geometric Average Return 1955-2003 All Stocks 12.55% 50 Best 5YPP 6.89% 50 Worst 5YPP 16.77% Strategy (from universe of Large Stocks) Geometric Average Return 1955-2003 Large Stocks 11.18% 50 Best 5YPP 8.11% 50 Worst 5YPP 14.16% Valuation Metrics – EV/EBIT Another is valuation metrics. A valuation metric is a metric designed to measure the value of a company relative to something else. Valuation metrics are a price tag. They are what you pay over what you get. I label this general category “valuation metrics” because the one metric I am most interested in is not the only valuation metric that predicts market outperformance. Most valuation metrics have significant predictive value. Low PE and Low PB were identified as having predictive value several decades ago and still have substantial predictive value (read: they still work). But there is one that works better than the rest and that is the Enterprise Value to Earnings before interest and taxes multiple or EV/EBIT. First, what is Enterprise Value? Enterprise value is the true economic price of an entire company. It is the company’s market capitalization (share price x number of shares), with adjustments for the cash, debt, and other obligations the company has. Second, what is Earnings before interest and taxes? This is the company’s bottom line, its net income, with interest and tax costs added back. This is done to make performance comparable. A company’s capital structure (the amount of debt and cash it has) changes and this can also be a point of difference between companies. If we want to compare the operating performance of a company with that of another company or its own performance in a prior year, we get rid of the interest and tax to isolate for what we’re trying to measure. Put simply, EBIT is a purer measure of the profitability of most companies’ operations than any other number on the income statement. Together, EV and EBIT create a very powerful metric because they are both very sound measures of what they independently seek to capture: price and profit. As I mentioned, EV/EBIT is a quite powerful metric. I’ve done the following backtest in Bloomberg: US stocks Excluding utilities and financials Market cap > $20mm Equal weight (about 200 holdings at any one time) 1 year holding period Annual rebalancing Lowest 10% of the market on EV/EBIT From 1995-2015 (furthest back I could go with the test) This strategy generated annual returns of 21.68% versus 9.43% for the S&P 500. There are some other predictors in there like the inclusion of micro-caps, which historically outperform, and equal weighting, which outperforms, but there’s nothing wrong with that given that I don’t size based on market cap in my accounts and am able to invest in micro-caps. The biggest issue with this test is the limited sample size of only 20 years, but that’s all I could get with the data I had. In Quantitative Value, Carlisle and Grey subject EV/EBIT to many things that are “proper” for academic studies, but unnecessary and really detract from performance, and yet EV/EBIT still performs really well. They also did the lowest 10% of the market on EV/EBIT and excluded utilities, financials, REITs, and ADRs, but they also: Excluded any company from the universe with a market cap less the 40th percentile on the NYSE which translated in the study to less than $1.4B in 2011 dollars Market cap weighted instead of equal weighted Nevertheless, they found that the strategy returned 14.55% annually over 48 years from 1964-2011, beating the S&P 500 by 5.03% per year. Further, the top decile (highest 10% of population on EV/EBIT) underperformed the market by 2.43%/yr, so there is a spread of about 7.5% in annual performance between the top and bottom deciles. The most meaningful takeaways there are that the predictive ability still holds up with rigorous testing and over many market cycles (almost 50 years is a good-sized sample). Finally, EV/EBIT is one of the metrics used in the Magic Formula. The Magic Formula takes the 3500 largest stocks by market cap in the US and assigns a number rank to each based first on return on invested capital, a profitability metric, and then on EV/EBIT. So each stock has two rankings. These rankings are added together. The 30 stocks with the highest (smallest number) combined rank are equal weighted and rebalanced annually. According to Greenblatt, this strategy did like 30% annual returns over almost 20 years ending around when the book was first published in. Note that it’s been a while since I last read the book so those numbers may not be precise, but the bottom line is that the results were really good. Some issues here are the limited sample size in terms of years and the size of the basket, but the results are still compelling. Studies trying to replicate Magic Formula have found that the inclusion of ROIC actually detracts from its performance. In other words, EV/EBIT’s predictive ability is driving more than 100% of the performance. But Why? So EV/EBIT and momentum both perform well. But why? I don’t think it is enough only to have historical predictive value. It also makes sense. The test I use is “would I look for this if I were analyzing any one stock or business for prospective purchase?” If it doesn’t make sense but looks good and we go with it, we assume a major risk: data mining. One study attempting to illustrate data mining found that 99% of S&P 500 movement over 12 years was predicted by butter production in Bangladesh. Correlation does not equal causation. Past predictive value does not equal future predictive value. Source: Forbes And this is why I really like EV/EBIT. It makes sense. If I were looking at an individual company, a low EV/EBIT would look very appealing to me. In fact, I often value stocks, in part, using this metric. It also makes sense that buying things at lower prices is a good strategy. Momentum does not make as much sense to me. Why buy things now when it’s gotten so much more expensive? Except for certain luxury items, the appeal of most products decreases as the price increases. Conclusion So this is a big part of my process. I screen based on EV/EBIT, generate a list of a few hundred companies, and go through them one by one. There is intuition involved, but I’d say the list generation process is pretty systematic. Both are important and I like where my strategy is positioned. There are elements of both deep analysis and disciplined rules in my process and I think that’s a good place to be. I don’t know if I’ll always be using EV/EBIT and I doubt it will always be my primary focus, but I think the more important point is to have a defined process that makes sense, and, for me, to stay positioned at the crossroads of active and passive investing, rules and intuition as the lines between these seeming dichotomies blur in the future.

The Time To Hedge Is Now! 2,753 Percent Profits On Men’s Wearhouse

Summary Introduction and a brief overview of the series. 2,753 percent profit since August. Taking profits or riding a little more? Discussion of the risks of employing this strategy versus not being hedged. Back to Bear Rally = Another Chance! Introduction and Series Overview If you are new to this series you will likely find it useful to refer back to the original articles, all of which listed with links in this instablog . In the Part I of this series I provided an overview of an inexpensive strategy to protect an equity portfolio from heavy losses in a market crash. In Part II, I provided more explanation of how the strategy works and gave the first two candidate companies to choose from as part of a diversified basket using put option contracts. I also provided an explanation of the candidate selection process and an example of how it can help grow both capital and income over the long term. Part III provided a basic tutorial on options. Part IV explained my process for selecting options and Part V explained why I do not use ETFs for hedging. Parts VI through IX primarily provide additional candidates for use in the strategy. Part X explains my rules that guide my exit strategy. All of the above articles include varying views that I consider to be worthy of contemplation regarding possible triggers that could lead to another sizeable market correction. I want to make it very clear that I am not predicting a market crash. Bear markets are a part of investing in equities, plain and simple. I like to take some of the pain out of the downside to make it easier to stick to my investing plan: select superior companies that have sustainable advantages, consistently rising dividends and excellent long-term growth prospects. Then I like to hold onto to those investments unless the fundamental reasons for which I bought them in the first place changes. Investing long term works! I just want to reduce the occasional pain inflicted by bear markets. We are already past the average duration of all bull markets since 1920. The current bull is now longer in duration (nearing 82 months) than all but two bull markets during that time period (out of a total of 15). The three longest bulls prior to this are 1949-1956 (70 months), 1921-1929 (97 months), and 1990-2000 (117 months). So, I am preparing for the inevitable next bear market. I do not know when the strategy will pay off, and I will be the first to admit that I am earlier than I suggested at the beginning of this series. I feel confident that the probability of experiencing another major bear market continues to rise. It may have started already or it may not come until 2017, before we take another hit like we did in 2008-09. But I am not willing to risk losing 30-50 percent of my portfolio to save the less than two percent per year cost of a rolling insurance hedge. I am convinced that the longer the duration of the bull market the worse the resulting bear market will be. I do not enjoy writing about the potentiality of down markets, but the fact is: they happen. I don’t mind being down by as much as 15 percent from time to time; that is just a hiccup in the buy-and-hold investing strategy. But I do try to avoid the majority of the pain from larger market drops. To understand more about the strategy, please refer back to the first and second articles of this series. Without that foundation, the rest of the articles in this series may not make sense and could sound more like speculating with options rather than an inexpensive way to protect your portfolio against catastrophic loss. 2,753 percent profit since August I originally recommended buying puts on Men’s Wearhouse (NYSE: MW ) back in my August Update to this series. The stock was then trading at a price of $58.49 per share. Friday, after the company slashed its outlook for the current quarter on weaker-than-expected sales, the shares closed at $22.65 per share. In August I recommended buying two put options expiring in January 2016 with a strike price of $0.75 or less for each $100,000 in equity portfolio value. The bid premium at that time was $0.55 and the ask premium was $0.75. If one was patient it was possible to buy those put option for even less that the $0.55 bid premium not long after the article was published. After the close on Friday, those MW $45 put options were bid at a premium of about $21.40. If we assume the worst case scenario of buying the put options at the ask premium of $0.75, then the profit that is available today is 2,753 percent [($21.40 – $0.75) / $0.75]. And, of course, there is the possibility of getting a better premium than the current bid being offered. My target price of $25.00 was achieved. Thus, I am suggesting that those who ventured into this positions at my earlier recommendation consider what to do next. This one is likely to continue to be very volatile for the next few days and weeks. Taking profits now or riding a little longer? There may be more profit available, but sometimes it does little good to get greedy. The stock could just as easily rebound next week and leave us wishing we had taken profits. Or, the stock could go even lower between now and January. However, it should be noted that if the stock were to remain at this level through the January 20th expiration date, the value of the put option would equate to about $22.00. That probably isn’t enough additional gain for which to wait. If the stock rebounded by 25 percent between now and then, rising to about $28.50, the options should expire at about a $16.50 premium for a potential profit of 2,100 percent; much less than we could lock in now. It really is not much of a tradeoff to be considered. Do we take the profit now and cover the cost of our other hedge positions for the year or do we hold onto the position and hope that the stock remains depressed or goes lower over the next 2½ months? That is a decision each investor needs to make for themselves. For my own portfolio, I intend to take the profits on my position and deploy the proceeds over the next couple of months in a hedge position for 2016. My sense is that MW should linger below $30 until January unless management guides higher due to increased holiday sales. Such an announcement is not likely to come until mid-December or later. We have already exceeded my target so I recommend taking what the market gives you now. This is no time to get greedy. I want to emphasize that this strategy is not a get rich quick plan. It is a hedge strategy to provide insurance against a major bear market. When I have the opportunity to take some profits and reduce the cost of my hedging strategy, I will often take at least some of what the market gives me. When one of our candidates implodes as MW just did and like MU and TEX did for us previously, it is prudent to take advantage of situation. I have tried to be clear from the beginning that the strategy has the potential to cost less than one percent of a portfolio value per year for this very reason. Any one of the candidates has the potential to surprise big to the downside over the life of the hedge, thereby helping to offset part or all of the cost of the hedge in any given year. It only takes one good plunge surprise to pay for the most of the cost of our total hedge for a year. The MW situation is just one more example of how that works. If an investor decides to employ this hedge strategy, each individual needs to do some additional due diligence to identify which candidates they wish to use and which contracts are best suited for their respective risk tolerance. I do not always choose the option contract with the highest possible gain or the lowest cost. I should also point out that in many cases I will own several different contracts with different strikes on one company. I do so because as the strike rises the hedge kicks in sooner, but I buy a mix to keep the overall cost down. To build such positions one would need to follow future articles as I provide the best option contracts on the best candidates each month. I build my own positions from the positions listed in the articles. Discussion of the risks of employing this strategy versus not being hedged. I want to discuss risk for a moment now. Obviously, if the market continues higher beyond January 2016 all of our earlier option (except JNK ) contracts could expire worthless. I am not ready to roll positions yet, but will probably when the open interest on contracts expiring in May, June and July have reached at least 50 or more. We need some liquidity to be able to move in and out of positions when necessary. I have never found insurance offered for free. We could lose all of our initial premiums paid plus commissions. If I expected that to happen I would not be using the strategy myself. But it is one of the potential outcomes and readers should be aware of it. And if that happens, I will initiate another round of put options for expiration in July 2016 or January 2017, using from one to two percent of my portfolio to hedge for another year. The longer the bulls maintain control of the market the more the insurance will cost me. But I will not be worrying about the next crash. Peace of mind has a cost. I just like to keep it as low as possible. Mine is a unique hedging strategy. But it is not the only hedging strategy that can work. Each investor needs to consider which strategy makes the most sense for their own purposes. The main reason I am writing these articles is raise the awareness of investors that hedging is a prudent part of an overall investment strategy. One does not need to be hedged at all times; that would be overkill and far too expensive. But when the equities market has been hovering at all-time record highs for months and the bulls have been in charge for as long as is the case in the current environment, investors need to consider whether they can stand another bear market without protecting against those losses. Because of the uncertainty in terms of how much longer this bull market can be sustained and the potential risk versus reward potential of hedging versus not hedging, it is my preference to risk a small percentage of my principal (perhaps as much as two percent) to insure against losing a much larger portion of my capital (30 percent or more). But this is a decision that each investor needs to make for themselves. I do not commit more than two percent of my portfolio value to an initial hedge strategy position and have never committed more than ten percent to such a strategy in total. The ten percent rule may come into play when a bull market continues much longer than expected (like five years instead of two or three). And when the bull continues for longer than is supported by the fundamentals, the bear that follows is usually deeper than it otherwise would have been. In other words, at this point I expect a correction greater than the original 30 percent that I originally forecast. If the next recession does not begin until the second half of 2016 or 2017, I would expect the next bear market to be more like the last two. If I am right, protecting a portfolio becomes ever more important as the bull market continues. As always, I welcome comments and will try to address any concerns or questions either in the comments section or in a future article as soon as I can. The great thing about Seeking Alpha is that we can agree to disagree and, through respectful discussion, learn from each other’s experience and knowledge.

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.