Tag Archives: undefined

Using ETFs To Short The Market

Summary The structure and pricing of inverse and leveraged ETPs is complicated. The principal investments of this fund are money market instruments and derivatives. Daily re-balancing can be a concern. Reading through recent Seeking Alpha articles regarding the broader market, lengthy and often heated discussions tend to develop on the direction of the market. That being said, it seems as if opinions on the eventual breakthrough of the current sideways trading range are about half and half, with maybe slightly more bulls. I also recently read comments on an article about leveraged and inverse exchange-traded products, where many readers did not seem to have a clear understanding of how (particularly) inverse ETPs are priced. In light of these two observations, I thought it pertinent to analyze how an inverse ETF is structured, for the benefit of investors who are considering investing in one in order to profit from potential downside movement. Due to the referenced uncertainty and volatility present in the overall market, I decided to use the ProShares Short S&P 500 ETF (NYSEARCA: SH ) as the subject matter for my analysis. SH is an inverse ETF that attempts to return -1x the return of the S&P 500 on a daily basis. How does it achieve inverse returns? SH achieves returns that are inversely correlated to the S&P 500 by investing in assets and derivatives that perform (or historically perform) well when the market is not performing well. There are four main investments used by ProShares in its inverse index ETFs, and these are: Swaps (derivative market) Futures (derivative market) U.S. Treasury Bills (money market) Repurchase Agreements (money market) Derivatives : The sale of swaps will benefit in a falling market, because the buyer of the swaps is required to pay the seller the amount that the underlying has fallen in price. Inverse exposure through futures is likely most often achieved by short-selling index futures. Money Market Instruments : The use of short-term Treasuries and other money market instruments relates to the fact that short-term debt historically performs inversely to the market. This is due to there being a “flight to safety” when the equity markets are falling. Daily Re-balancing: For periods longer than a single day, the Fund will lose money when the level of the Index is flat, and it is possible that the Fund will lose money even if the level of the Index falls. – SH Prospectus The effect of daily rebalancing is one of the primary misunderstandings regarding inverse or leveraged ETFs that I see on Seeking Alpha. People discuss how they will “invest” in a leveraged ETF and hold it for several weeks, months, or even years in some instances. It is important to recognize that this is not the intended purpose of this type of ETF. These are intended to be traded for short time periods. In order to maintain the proper leverage ratio, inverse returns, and index exposure, SH is rebalanced each day. What this means for an investor is simple to illustrate: Suppose that at the end of the trading day on Monday, you invest $1,000 in a -1x inverse ETF @ $100 per share. The ETF tracks an underlying index with a value of $5,000. At market close on Tuesday, the index has decreased 10% to $4,500. In turn, the ETF has risen 10% to $110. By the close on Wednesday, the index has recovered to the original $5,000 – a roughly 11.11% gain. In turn, the ETF now loses 11.11%, which brings the value of your position to $97.78. Even though the index is exactly the same value as it was when you initiated the position, your position has lost money. This effect is also known as beta slippage. Note: This could theoretically work to your advantage, should the opposite situation occur. Conclusion: Simply by looking at a chart of SH, you will see that if you had held it for the duration of the 2008 collapse, you would have indeed profited: ND data by YCharts However, the return ratio was not accurate, with SH gaining approximately 26.07% from 1st January, 2007 to the first peak and SPX losing approximately 43.25% in the same time frame. In short, an inverse ETF like SH can be a great way to hedge short-term volatility or for intra-day trading, but if an investor is looking to actually short an asset (in this case, the S&P 500) for a long-term position, then it is not the most effective way to do so. Hopefully, with a clearer understanding of how this ETF is structured, prospective investors can make better use of it as a tool for his or her portfolio. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. Additional disclosure: This article is not intended to offer a recommendation to buy or sell any particular asset, and does not reflect the author’s opinions on the direction of the market. It is simply intended to provide an overview of how a complicated but useful financial instrument works.

Pursuing Smart Beta: Multi-Factored ETFs

Summary Both iShares and Global X have recently issued ETFs based on selecting holdings using complex systems of implementing “smart beta.” iShares’ “FactorSelect” funds use a complex system of factors to select high-quality holdings. Global X’s “Scientific Beta” funds use a complex weighting system to develop a portfolio that will perform optimally. In June, I wrote about the iShares FactorSelect MSCI International ETF (NYSEARCA: INTF ), an ETF with an inception date of April 28, 2015. 1 iShares introduced four other funds on that same date: the iShares FactorSelect MSCI Intl Small-Cap ETF (NYSEARCA: ISCF ), the iShares FactorSelect MSCI Global ETF (NYSEARCA: ACWF ), the iShares FactorSelect MSCI USA ETF (NYSEARCA: LRGF ) and the iShares FactorSelect MSCI USA Small-Cap ETF (NYSEARCA: SMLF ); as the names would suggest, all of the funds have something in common: they are based on the FactorSelect strategy. Shortly after the iShares releases (May 12, to be exact), Global X Funds issued four ETFs of its own: the Global X Scientific Beta U.S. ETF (NYSEARCA: SCIU ), the Global X Scientific Beta Europe ETF (NYSEARCA: SCID ), the Global X Scientific Beta Asia ex-Japan ETF (NYSEARCA: SCIX ), and the Global X Scientific Beta Japan ETF (NYSEARCA: SCIJ ). Again, as the names suggest, all four ETFs are based on the same ( Scientific Beta ) strategy. Both series of funds represent efforts by their respective issuers to provide investors with a “bridge” between – or perhaps better: an alternative to – actively managed funds and purely passive funds. 2 I thought it might be interesting to take a look at the two sets of offerings and their underlying strategies; there are interesting similarities between the approaches, as well as some significant differences. FactorSelect BlackRock’s (NYSE: BLK ) approach to Smart Beta involves a (proprietary) model that selects companies for a given fund on the basis of four factors: 3 Value : a score is derived from a company’s valuation, using measures such as price/book value, forward share price to earnings, enterprise value to operating cash flow, etc. Quality : a score is computed using a company’s metrics (return on equity, debt/equity, earnings variability, etc.). Momentum : the score is a composite of the security’s relative performance against the global market (for two years), and against securities based in the same country (for six months and twelve months). Low Size : the score is a measure of a company’s market capitalization compared to companies based in the same country. The final score is a composite of the four factors, which are modified-capitalization weighted. The funds are rebalanced/reconstituted semi-annually. Scientific Beta As with BlackRock, Global scores its potential holdings on the basis of four factors, although each factor has a single definition, rather than being an aggregate of several “sub-factors.” 4 , 5 Thus: Value : Price to Book Size : Market Capitalization Low Volatility : 104-week historical volatility Momentum : last 52-week total return, excluding most recent month. Unlike BlackRock’s modified-cap-weighting scheme for its factors, Global employs a more complex weighting system incorporating five schemes: 6 Maximum Deconcentration : equal weights to minimize firm-specific risk. Maximum Decorrelation : minimizes volatility based on historical correlations between holdings. Diversified Risk Weighted : volatility and weight are inversely correlated (greater volatility gets lesser weight) Efficient Minimum Volatility : minimize portfolio volatility based on both correlations and volatilities. Efficient Maximum Sharpe Ratio : maximize risk-adjusted performance based on expected returns and volatilities. The funds are rebalanced/reconstituted quarterly. Comparison In essence, both sets of funds mimic an actively managed fund by introducing complex selection and weighting criteria. The use of complex criteria adds a discretionary element to holdings selection, while at the same time providing the same level of transparency that is typical of an index fund. The difference between the two sets of holdings is in the focus of the complexity they introduce, with iShares introducing the complexity in the scoring factors and Global using a complex weighting schema. I will argue that while their approaches are different, the results may – at least intuitively – be quite similar. 7 Factors Both sets of funds look at a company’s size , value and momentum . Where they differ, iShares factor involves data reflecting a company’s quality , while Global factors the volatility of a company’s stock. But even where the two series “agree,” the agreement is in name only. Global defines its factors very narrowly, choosing a single characteristic, ratio or formula for each factor, while the iShares funds’ factors are complex aggregates. The difference in factor composition is perhaps most starkly exhibited in terms of stock value . It is difficult to accept that one can determine the valuation of a company on a single metric, particularly price-to-book. 8 Finding a realistic way to combine a set of valuation data to arrive at a reasonable value seems more adequate. Both funds incorporate size in their factoring, where they seek to avoid selecting large companies. Statistically, smaller companies realize more growth than large companies do, so in the interest of maximizing portfolio growth potential both fund series place greater value on the smaller companies. It is with regard to size that both companies structure their portfolios differently. iShares divides its FactorSelect funds according to U.S., ex-U.S. Developed, and Global focuses, with the first two subdivided between large- and mid-cap portfolios on the one hand, and small cap portfolios on the other. Global structures its four portfolios to include all market caps in each. Momentum is an interesting case, with Global focusing on a company’s total returns over 52 weeks, while iShares considers a company’s performance in relation to ((a)) global stocks over two years, and ((b)) same-country companies over six- and 12- month periods. Thus, while Global is looking at each company individually, iShares evaluates companies in the context of global and national trends – a more dynamic picture of how well a company is doing. I must admit to a little dismay that Global does not take fundamentals into account. One of iShares’ factors (quality) is essentially a composite of a company’s fundamental data, and would seem to be (at least to my view) much more important in evaluating a company’s prospects than low volatility, which replaces fundamentals in the Global schema. Moreover, and as we will see next, volatility plays an important role in Global’s weighting schema – using it in two phases of its selection process would seem excessive to its overall importance. Weighting Comparison While iShares’ factors are complex aggregates, the series becomes somewhat one dimensional in terms of its weighting system, which is a “modified market-cap weighting,” the weight a holding receives being its capitalization influenced by its overall factor score. Global’s factors may be one dimensional, but its weighting system is decidedly not. Global’s five-element weighting process is designed to identify correlations and volatilities in its portfolio, adjusting the weighting of its holdings (from an initial equalized weighting) to compensate for intra-portfolio correlations and minimize overall portfolio volatility. Finally, weighting is adjusted according to the companies’ Sharpe Ratios. Assessment In general, the primary difference between the two sets of funds seems to be that the iShares FactorSelect funds have portfolios that are constituted very selectively, while the Global X Scientific Beta funds have portfolios that are balanced for optimal performance. It will be interesting to see which series fares better over time from this perspective, but I do not see enough significance in this difference to recommend one series over the other. 9 Both systems are crafted to produce a portfolio that should exhibit excellent growth patterns, although they accomplish that goal differently. What is significant is the focus of each fund in both series. Global has developed funds that are geographically specific – one for the U.S., one for Europe, one for Japan and one for “non-Japan” Asia. If the investor is interested in a nicely balanced portfolio for a specific region, the Scientific Beta series makes for an attractive option. The iShares funds, on the other hand, constitute very selective groups of companies that provide excellent growth potential – at least, in principle. The funds are differentiated according to the extent of that potential: the relative safety of mid- to large-capped holdings or the greater potential growth of small caps; they are further differentiated by whether the holdings are domestic or international. The investor is choosing an equity category only broadly differentiated regionally. As I mentioned in my article on INTF, I have been really reluctant to commit to an international ETF. INTF is the first fund that has appealed to me in terms of its selectiveness and the range of its holdings. The fund has enabled me to add solid international holdings to my portfolio while making me feel confident that the foreign holdings are high quality companies with good growth prospects. Disclaimers This article is for informational use only. It is not intended as a recommendation or inducement to purchase or sell any financial instrument issued by or pertaining to any company or fund mentioned or described herein. All data contained herein is accurate to the best of my ability to ascertain, and is drawn from the Company’s Prospectus, Statement of Additional Information, and fact sheets. Data from any other sources (if used) is cited as such. All opinions contained herein are mine unless otherwise indicated. The opinions of others that may be included are identified as such and do not necessarily reflect my own views. Before investing, readers are reminded that they are responsible for performing their own due diligence; they are also reminded that it is possible to lose part or all of their invested money. Please invest carefully. 1 ” INTF: An Ideal Basket Of International Equities ,” Seeking Alpha , June 9, 2015. 2 Both companies specify that their strategies are intended to provide viable alternatives to actively managed funds while outperforming the simple indexed funds. 3 These factors are described in detail in the prospectuses(under “Principal Investment Strategies”) for each of the funds. All five funds apply the same factors, each fund applying them to a specific regions/market capitalization. The calculations in the model are proprietary to MSCI, which develops the underlying index for the funds. 4 Global X Scientific Beta ETFs Family Guide , available here . 5 A detailed discussion of the construction of the indices used by Global X can be found in ERI Scientific Beta Equity Strategy Construction Rules , by the EDHEC-Risk Institute . 6 Scientific Beta ETFs Family Guide . 7 Similar, at least, in terms of ultimate approach, although not necessarily in terms of actual portfolio content. 8 I am a big fan of price-to-book value. It is a great indicator of value, and I regularly look at companies that have P/Bs less than 1 – these are companies where the value of the actual assets of the company exceed the value of the shares. In the event of a bankruptcy, shareholders of such a company would, in principle, receive more money for their shares than they paid. However, I would not rely on P/B as the determinant of a company’s overall value. 9 Actually, I do, but it is a matter of personal preference, rather than rigorous analysis. The FactorSelect process seems more comprehensive, indicating a greater level of sensitivity to the strength (both fundamental and displayed) of the companies of which the portfolio is comprised, while the Global X Scientific Beta system – while rich in its weighting system – seems rather sparse in selectiveness. The result of the latter system is a portfolio that may be well-balanced, but I am less confident in the components of that portfolio. Disclosure: I am/we are long INTF. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

The Low Volatility Anomaly: Overconfidence Bias

This series offers an expansive look at the Low Volatility Anomaly, or why lower risk stocks have historically produced stronger risk-adjusted returns than higher risk stocks or the broader market. This article hypothesizes that cognitive biases like the overconfidence bias contribute to the Low Volatility Anomaly. Like previous pieces in this series, this article covers a deviation between model and market that may contribute to the outperformance of low volatility strategies. A cognitive bias is a deviation in judgment where people draw inferences in an illogical fashion. As discussed in the last piece in this ongoing series on lottery preferences , cognitive biases can impact investment decision-making and likely contribute to the Low Volatility Anomaly. This article will discuss an additional cognitive biases that could contribute to this phenomenon – the overconfidence bias. The overconfidence bias suggests that a person’s subjective confidence in their own judgment is reliability greater than the objective accuracy of those judgments. The most oft cited example of the overconfidence bias is the 1980 finding by Ola Svensson that ninety-three percent of American drivers rate themselves better than the median. Traveling back from the highway to the Capital Asset Pricing Model (CAPM) assumptions, the model calls for homogeneous investor expectations, including expected values, standard deviations, and correlation coefficients. Valuing investments necessarily involves forecasting as a means to assessing tradeoffs between risk and return. Empirical evidence suggests that most people form confidence intervals that are too narrow. Borrowing from studies by Fischoff, Slovic, and Lichtenstein (1977) and Alpert and Raiffa (1982) , I conducted a similar study during a lecture to a fixed income class at my undergraduate alma mater in the fall of 2014. The students were asked a set of ten questions with numeric answers and asked to bound their answer by a confidence interval such that there was a ninety percent chance that their numeric answer would fall within the range. The ten questions were as follows: What is the population of the state? What is the seating capacity of the football stadium? What is number of different undergraduate majors at the University? What were the revenues of the athletic program in the previous year? What was the number of degrees conferred in the most recent academic year? What is the current yield level of the 30-year Treasury? What is the size of the U.S. Gross Domestic Product? What was the total number of jobs created in the U.S. in 2014? What was the total number of automobiles sold in the U.S? What is the U.S. Median Household Income? At a ninety percent confidence interval, half the class should have had nine or more results inside their confidence interval. Of the roughly thirty-five students, none had nine. Or eight. Or seven. Or six. Two students had five of their answers inside their bounds, but most of the students had between two and four. The class was overconfident. The first five questions were on the world around them at college, and the second five questions were on basic economic statistics. These topics should have yielded far better forecasts than the multi-year prognostications of market or security variables inherent in investment selection. The students did poorly – as poorly as the author when he first completed a similar exercise. The point of the exercise (aside from breaking up the monotony of my lecture) was to illustrate the overconfidence bias to the class. Overconfidence can drastically damage investment returns. Given the geographic proximity of the university to some of the nation’s leading onshore oil and gas resources, the rapid and unexpected drawdown in oil prices at the time of the lecture and the related implications on energy-related assets proved salient to the audience. Additional examples of the overconfidence bias given in the lecture included persistent overestimates of economic growth from the International Monetary Fund and Federal Reserve post-crisis, and the poor job of private and public sector economists at forecasting long-term interest rates, which were at the time rallying sharply in the face of consensus estimates for rising rates. Like the students surveyed, professional investors have proved similarly overconfident. Active managers implicitly assume that they are capable of beating their benchmark despite long-run evidence demonstrating that the average active manager fails to accomplish this feat on average over time ( Fama, French 2009 ). The collective overconfidence by the cadre of active managers violates that CAPM assumption of rationality and could be a factor that contributes to the Low Volatility Anomaly. If a manager is truly as skilled as they believe, then participation in higher volatility segments of the market offer the largest return proposition to capitalize on their perceived skill. If that same manager believed that the market was likely to fall, then they would not choose to invest in low volatility assets, which would outperform on a relative basis, but choose to exit the market entirely to outperform on an absolute basis. This overconfidence bias then likely contributes to the outperformance of low volatility stocks (referenced by SPLV ) relative to high beta stocks (referenced by SPHB ) depicted in the introductory article to this series . Further connecting the overconfidence bias to investment returns, we see more activity from market optimists than pessimists. Perhaps married to the market frictions inherent in the Leverage Aversion Hypothesis , the market in general is far less likely to short high volatility assets than it is to buy them. With skeptics more often sidelined than short, high beta assets with a more diffuse set of opinions on forward returns will then have more optimists among their holders, potentially pushing prices higher and future returns lower. In coming articles, I will highlight additional empirical evidence on the Low Volatility Anomaly, including utilization by a great investment mind, examples in fixed income, and examples crossing over between the equity and fixed income markets. I will then feature some ways in which Seeking Alpha readers can look to exploit the Low Volatility Anomaly in their portfolios. Disclaimer My articles may contain statements and projections that are forward-looking in nature, and therefore, inherently subject to numerous risks, uncertainties and assumptions. While my articles focus on generating long-term risk-adjusted returns, investment decisions necessarily involve the risk of loss of principal. Individual investor circumstances vary significantly, and information gleaned from my articles should be applied to your own unique investment situation, objectives, risk tolerance, and investment horizon. Disclosure: I am/we are long SPLV. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.