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Gain From Market Correction Via Inverse ETFs

Though the U.S. stock market rode past the nagging Greek debt drama in July, occasional sell-offs in China and severely low oil prices this year, it lost steam in recent sessions. A wavering Chinese economy and the consequent burst of Chinese stock bubbles on the one hand and dimmed chances of the Fed’s sooner-than-expected policy tightening on the other flared up global growth worries and led the markets go into a tailspin. In China, trading has been rocky for long. The Chinese policy makers devalued the currency yuan by 2% presumably to maintain export competitiveness, on August 11. While this hinted at a deepening economic crisis, the release of the flash Chinese manufacturing data (for August) which indicated a six-and-a-half year low number was the final nail in the coffin. Though China sought to restrain the rout by allowed the pension funds to invest about $97 billion in the market, there was no relief in store. Uncertainty in China and lack of precision by the Fed on policy tightening timeline roiled the market momentum and ravaged most risky asset classes. Most importantly, oil prices slipped below $40/barrel on concerns over reduced demand. All these wrecked havoc on global equities and commodities. As per Bank of America Merrill Lynch, equity outflows touched a 15-week high . U.S. Stock-Index futures recorded the deepest weekly plunge in four years in the week ended August 21, 2015. The S&P 500 index is now down 7.6% from its May high and Dow Jones Industrial Average plummeted about 10.3% since it hit a high in May thanks mainly to a freefall in oil prices. NASDAQ Composite also slipped 10% from this year’s high touched in July. Persuaded by the Chinese market carnage, Asian stocks approached a three-year low, commodity prices dived to a 16-year low, while credit risk in Asia rose to the highest level since March 2014. Emerging market equity funds witnessed a flight of capital worth over $6 billion and remained in red for seven straight weeks. Equity market correction this time looks graver as the sentiment has turned more bearish of late due to heightened uncertainty and a slew of negative news in Europe and Japan too. Japan’s Q2 GDP data was downbeat while an imminent snap election in Greece, the epicenter of the Euro zone debt crisis, has increased the risk of volatility in the coming days. Notably, the CBOE Volatility Index (VIX), a fear gauge which measures investor perception of the market’s risk, added over 27% in the last five trading sessions (as of August 21, 2015). While there are several options available in the inverse equity ETFs space, we have highlighted five ETFs that are widely spread across geographies and sectors. These products provided handsome returns over the trailing five-days and one-month period and are expected to continue doing so, especially if the current bearishness persists in the months ahead. ProShares Short Dow 30 ETF (NYSEARCA: DOG ) This product seeks to deliver inverse exposure to the daily performance of the Dow Jones Industrial Average, which includes the 30 blue chip companies. The fund has managed $311 million in its asset base while charging 95 bps in fees and expenses. Volume is moderate as it exchanges more than 700,000 shares per day on average. DOG gained over 5.8% over the past one week and 7.8% in the last one-month frame (as of August 21, 2015). ProShares Short QQQ ETF (NYSEARCA: PSQ ) The fund looks to track the inverse of the day performance of 100 largest domestic and international non-financial companies listed on the tech-heavy NASDAQ. This $277 million-product charges 95 bps in fees and added 7.5% in the last five trading sessions and 9.4% in the last one month (as of August 21, 2015). ProShares Short S&P 500 ETF (NYSEARCA: SH ) This fund provides inverse exposure to the daily performance of the S&P 500 index. It is the most popular and liquid ETF in the inverse equity space with AUM of nearly $1.7 billion and average daily volume of around 3.6 million shares. The fund charges 90 bps in annual fees and added nearly 5.3% in the last five trading sessions and 6.7% in the last one month (as of August 21, 2015). ProShares Short MSCI Emerging Markets ETF (NYSEARCA: EUM ) Since the recent upheaval was global, a look at the emerging markets is warranted. The product seeks to track the opposite of the daily performance of the MSCI Emerging Markets Index. This $461.4-million product trades at volumes of 600,000 shares a day and charges 95 bps in fees. EUM was up 6.7% in the last five days and 15.5% in the last one month. Direxion Daily CSI 300 China A Share Bear 1X Shares (NYSEARCA: CHAD ) As China was the root cause of this massacre, the region offers immense scope to gain via inverses equity ETFs. Having debuted in June 2015, CHAD seeks daily investment results of 100% of the inverse of the performance of the CSI 300 Index. The index is market cap weighted and comprises the largest and most liquid stocks in the Chinese A-share market. Barely a few days old, the fund has already amassed over $320 million in assets. The fund charges 95 bps in fees and was up about 16% in the last five days. Over the last one month, the fund added over 15%. Link to the original post on Zacks.com

Shock And Horror: Passive Hedge Funds

An academic article entitled “Passive Hedge Funds” has recently attracted quite a lot of comment in the Financial Times, Bloomberg, and on a variety of websites. Those whose ambition in life seems to be to discredit hedge funds and their managers at every turn have, of course, latched onto it. But the paper’s title is tendentious, its argument familiar and in some places flawed, and its conclusions really quite anodyne. Investors seeking hedge fund-like exposure through liquid alternatives will find that some products are similar to those described in that article; they should examine them very carefully before investing. The purported humor of math jokes often depends on the technical use of a term that has other, more familiar meanings. Thus, my college roommate’s knee-slapper about how every integer is interesting relied on a definition of ‘interesting’ as ‘having a unique property.’ The joke took the form of a mathematical induction: 1 is the multiplicative identity, 2 is the only even prime, 3 is the lowest true prime, 4 is the lowest perfect square… so if there is an uninteresting integer, it is interesting, because it is the lowest one. Maybe you had to be there. I am reminded of this moment of boundless mirth by a paper entitled ” Passive Hedge Funds ,” by Mikhail Tupitsyn and Paul Lajbcygier. This title has, inevitably, attracted comment, including headlines such as “Study: Hedge Funds Don’t Do S**t, Suck” (gawker.com) or, with less sophistication and élan, “New Study Argues Hedge Funds are an Even Worse Scam than We Thought” (vox.com) and even more prosaically, “The Case Against Hedge Fund Managers” (ai-cio.com). With the apparent exception of the latter, these commentators were so enamored by their deeply considered wisdom that they clearly felt no need to read the paper. Because its authors are quite explicit about their idiosyncratic use of the term ‘passive.’ They even put scare-quotes around it. The commentators just missed the punchline. It is hard to dispute Humpty Dumpty: “When I use a word, it means just what I choose it to mean ─ neither more nor less.” Since they take pains to explain what they mean by it, I have no argument with the authors’ use of ‘passive.’ They might have used ‘hippopotamus,’ which is more euphonious, but lacking poetic souls, they chose ‘passive,’ and missed the opportunity for a great title. The sense in which the authors use ‘passive’ to describe hedge fund return patterns is that they have linear correlation to hedge fund β. The crux of their argument is that “A manager with genuine investment skill should not only have “passive” linear risk exposures to alternative risk factors ( i.e ., alternative beta) but should also produce enhanced returns through nonlinear ‘active risk exposures.'” This is contentious, as will be seen below, but it is simply posited as a truth rather than justified. Was their choice of ‘passive’ tendentious and self-promoting? Of course: how else would a postdoc and an associate prof at Melbourne’s #2 university get noticed in the Financial Times or Bloomberg, let alone a temple to the Muses such as gawker.com? Was it helpful? Our commentators’ complete failure to understand the authors’ intent makes it rather obvious that it was not. The Tupitsyn and Lajbcygier article is, as their review of the literature makes clear, one of a long line of academic studies that propose models for hedge fund returns. Even critics more competent than our commentators tend to latch onto these studies as “proof” that hedge funds offer little value-added. But anything can be modeled ─ conventional mutual funds, sunspot frequencies, even (allegedly) the earth’s climate. Problems arise when, as Emanuel Derman and others have noted, the models are mistaken for reality. And hedge fund β ─ against which the authors argue hedge fund managers fail to add value ─ is, at best, a very peculiar concept, and arguably a spurious one. On consideration, the authors’ argument begins to look strangely circular: hedge funds fail to add value relative to metrics that derive from their own returns. This is something like arguing that I am a lousy swimmer because I am unable to swim faster than myself. I may well be a lousy swimmer, but comparison with my own performance will not establish that. A good portion of Tupitsyn’s and Lajbcygier’s analysis is devoted to returns on hedge fund indices. In choosing these as a database, they, like many before them, commit the fallacy of composition. The fact that you can calculate a mean return from a pile of reports does not indicate that there is such a thing as an average hedge fund: it is not only possible, but likely that none of the funds analyzed exhibited the mean return. Further, there is no reason to expect continuity from one time period to another: a fund whose return was close to the center of the distribution in one period may be an outlier in the next. Hedge fund returns are widely dispersed both synchronically and over time, so that the value of hedge fund indices is pretty much restricted to service as performance metrics for specific time periods. The standard error of the mean = s/√n, where ‘s’ is the σ of the population and ‘n’ is its size. Obviously, the error is significantly higher and thus the epistemic value of the mean significantly less, the more dispersed the population is. Given the wide dispersion of hedge fund returns, the value of their average is largely restricted to the bragging rights it gives to marketers fortunate enough to work for funds that have outperformed it. The authors are aware of these limitations, and devote some analysis to the returns of individual, real world funds. They find that most funds have strong linear exposures to familiar factor influences on investment returns. They conclude that “The nonlinear risk is more pronounced in arbitrage styles and styles following multiple strategies, and it is weaker in directional styles.” This should hardly be surprising ─ arbitrage is inherently non-linear ─ and it is not at all clear why the presence of linear risk in other sorts of strategies should somehow suggest dereliction of duty on the part of their managers. If, for example, a dedicated short fund carried no (negative) equity exposure, its investors would certainly have reason to object! Admittedly, fewer long/short funds make use of their ability to add value by adjusting their net exposure than might be expected, and with relatively stable long/short ratios, their exposure to equity risk factors would, of course, be linear. The same would be true of any long-only equity fund, and would certainly not attract criticism. In fact, long/short funds have increasingly tended to pursue a trading-oriented (“risk on/risk off”) response to changes in their risk perceptions in place of making changes to their short positions. As a group, hedge funds provide us with ample reasons to criticize them. Despite declining over the last few years, fees are in most cases still too high for the service provided. Lack of transparency inhibits rational analysis and portfolio construction, while providing a breeding ground for a wide range of abuses and sharp practice. The artificial mystique that this opacity fosters is repulsively reminiscent of Ozma of Oz. However, neither an adolescent potty-mouth nor accusations of fraud are not needed to make these points forcefully and to draw the appropriate conclusions for investors. Nor are “discoveries” that hedge fund α is not a matter of otherworldly powers to bend the laws of economics to the manager’s will ─ that their skills might be very similar in both nature and quantity to the skills that conventional portfolio managers exhibit. Tupitsyn and Lajbcygier have made a small contribution to the growing literature on hedge fund replication ─ nothing less, but certainly nothing more. Theirs is only one approach to hedge fund replication, and to my mind a less than satisfactory one. Factor replication is an inherently backward-looking approach to modeling, and when applied to the return streams from hedge funds, likely to result in some rather peculiar portfolios. A technique that I suspect has much more promise is the creation of robo-managers ─ algorithmic trading techniques that mimic the trading strategies hedge funds are known to pursue. Many hedge funds, particularly CTAs, are already effectively automated. While it is illegal to steal their code, it is possible to imitate it based on an analysis of their returns. In considering an investment in liquid alternative funds, many of which are “quantitatively-driven” in ways that are rarely specified explicitly and require research to understand, the nature of the security selection technique should be given careful consideration. Approaches similar to that of Tupitsyn and Lajbcygier are worth a look, but may not deliver all that they promise; the source of the factor exposures they purport to imitate must be investigated. 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.

How I Got Burned By Leveraged ETNs

Summary During oil’s first slide earlier this year, I started trading UWTI. Initial success was of course followed by huge losses through the decay in pricing that tends to occur in leveraged funds. Let my experience be a lesson to all those considering messing with leverage. The beginning was great. I made the ultimate mistake. I bought based on what I “wanted” to happen, as opposed to what was “actually” going on. I first bought the VelocityShares 3x Long Crude Oil ETN (NYSEARCA: UWTI ) back in March for $2.24. Less than a month later I sold at $2.53. As the Exchange traded notes kept climbing I bought back in again at $2.82 and sold at $3.20. Suddenly, I was hooked on the volatility of oil ETFs and ETNs. For those who aren’t familiar, ETNs or “exchange traded notes” are sort of the risky cousins of ETFs. They’re unsecured debt securities. They offer a way to get in on the short term moves of whatever the commodity is that it represents. In UWTI’s case, it also offered leverage. Leveraged 3x, I was making basically making 9% off of every 3% gain in the oil index that the ETN tracks. I had some pretty cool returns going off of this bad boy. Oil was recovering from the first time it was down in the low $40/high $30 range. I was having so much fun with the returns that I turned a blind eye to the long term risks involved with ETNs. You see, if you screw up with a leveraged ETN trade, you need to just cut your losses and sell quick. There’s always a catch Canary Cash has a small article touching on the decay involved in leveraged ETNs. If you note Canary’s simplified chart example below, you can see the effects that percentage change has on pricing. ( Source ) Big shoutout to Canary Cash. Back to my downfall… After my success with UWTI, I thought I could keep it going. Oil kept climbing, so I kept buying. My last successful purchase was at $3.42, and I sold at $3.61. Emboldened, I completely ignored the fact that declining rig counts wasn’t having much affect on supply increases in crude. I bought in again at $3.64 believing the sky was the limit. Was it a dumb buy? Absolutely not. The mistake was not paying attention. Due to the big unrecoverable hits you can take on leveraged ETNs, you have to watch them closely and cut your losses quick if you start to lose. I was over confident. My past success had me thinking I couldn’t lose with this wonder security. Two weeks later, oil was in the beginning of its next downtrend…and I was kicking myself for losing a big portion of my previous gains. Did I take my medicine, cut my losses and sell? You all know the answer to that one. I waited. I thought “maybe oil will jump back up and I’ll get it all back”. Did it happen? You know the answer to that one too. UWTI’s chart says it all…. (source: nasdaq.com) Lesson Learned….. Today, UWTI is trading at just around $1. Decay, combined with oil’s downward spiral killed me. I sold last week in pure disgust with myself. It was my first experiment with leverage and it will The lesson I took away is just not to mess with leveraged anything ever again. If you don’t want to heed that warning, at least take my mistake as a clear indicator that if you buy a leveraged security and you lose, just cut your losses so you don’t lose big. Sigh….it still hurts to talk about it. 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.