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ETF Update: Smart Beta Launches As Far As The Eye Can See

Welcome back to the SA ETF Update. My goal is to keep Seeking Alpha readers up to date on the ETF universe and to gain some visibility, both for the ETF community and for me as its editor (so users know who to approach with issues, article ideas, to become a contributor, etc.). Every other week (depending on the reader response and submission volumes) we will highlight fund launches and closures for the week, as well as any news items that could impact ETF investors. As you might have noticed from the title, smart beta funds were on my mind this week. This might have something to do with the last 8 launches falling into that self-proclaimed category. It might also be due to a great read from Abnormal Returns, ” Finance blogger wisdom: smart beta bubble? ” In the linked article the author presented the following question to his online peers: The ‘smart beta’ or factor-investing bubble seems to be in full bloom. Is ‘smart beta’ simply the new active investing? If so, what happens to the entire fund industry which was built on the high fees associated with active management? This is a question that many have also covered on Seeking Alpha, but the most recent example is from Benjamin Lavine, CFA , whose article was posted on Wednesday (3/30). I would highly recommend it for any readers wondering what is behind the smart beta trend and how to interpret the term when considering an investment. With that disclaimer aside, let’s jump into the most recent round of smart beta launches: Fund launches for the week of March 21st, 2016 Principal expands into smart beta (3/22): The Principal Price Setters Index ETF (NASDAQ: PSET ) and the Principal Shareholder Yield Index ETF (NASDAQ: PY ) are the first smart beta launches from Principal Funds; both target mid- and large-cap domestic firms. However, PSET “focuses on companies with sustainable pricing power, consistent sales growth, high/stable margins, quality earnings, low leverage, and high levels of profitability,” while PY is for investors more concerned with “sustainable shareholder yield, strong cash flow generation, and capacity to increase dividends and/or buybacks.” Both funds are a relatively large departure from the Principal EDGE Active Income ETF (NYSEARCA: YLD ), which was launched in July 2015. This first venture into ETFs is an active fund investing across multiple income-producing asset classes in search of high-income investments. Victory Capital Management rolls out an emerging market fund (3/23): The Victory CEMP Emerging Market Volatility Wtd Index ETF (NASDAQ: CEZ ) was the third smart beta launch of the week. The in-house CEMP Emerging Market 500 Volatility Weighted Index “combines fundamental criteria with volatility weighting to seek to improve an investor’s ability to outperform traditional indexing strategies.” It is worth noting that the top countries represented at this time are Taiwan, China, South Korea and India; all of which are still considered emerging by MSCI , but many have argued that they are quickly evolving out of the traditional definition. Fund launches for the week of March 28th, 2016 Fund closures for the weeks of March 21st and 28th, 2016 Direxion Value Line Conservative Equity ETF (NYSEARCA: VLLV ) Direxion Value Line Mid- and Large-Cap High Dividend ETF (NYSEARCA: VLML ) Direxion Value Line Small- and Mid-Cap High Dividend ETF (NYSEARCA: VLSM ) ALPS Sector Leaders ETF (NYSEARCA: SLDR ) ALPS Sector Low Volatility ETF (NYSEARCA: SLOW ) ALPS STOXX Europe 600 ETF (NYSEARCA: STXX ) Global Commodity Equity ETF (NYSEARCA: CRBQ ) iSharesBond 2016 Corporate Term ETF (NYSEARCA: IBDA ) iSharesBond 2016 Corporate ex-Financials Term ETF (NYSEARCA: IBCB ) Have any other questions on ETFs or ETNs? Please comment below and I will try to clear things up. As an author and editor, I have found that constructive feedback is the best way to grow. What you would like to see discussed in the future? How can I improve this series to meet reader needs? Please share your thoughts on this first edition of the ETF Update series in the comments section below. Have a view on something that’s coming up or a new fund? Submit an article. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

In Which I Answer A Question About The Volatility ETNs

The prevailing wisdom on the volatility ETNs, VelocityShares Daily Inverse VIX Short-Term ETN (NASDAQ: XIV ) and iPath S&P 500 VIX ST Futures ETN (NYSEARCA: VXX ), is that XIV will rise over time and VXX will fall as long as the term structure is in contango more often than it’s in backwardation. A recently elapsed period, slightly longer than a year, makes apparent that’s not the case. Over the period from 2-Mar-2015 to 18-Mar-2015, both XIV and VXX experienced substantial net losses. VXX declined -27.5%, while XIV declined -29.9% (Figures 1 and 2). Figure 1. XIV prices Figure 2. VXX prices This loss for both ETNs over a prolonged period occurred while the term structure was in contango 73% of the time – 2.7X more often than it was in backwardation, as Figure 3 shows below. Why is that? Click to enlarge Figure 3. Percent Contango from 2-Mar-2015 to 18-Mar-2016 One way to answer this question is by reference to variance drain. I picked the period 2-Mar-2015 to 18-Mar-2015 for illustration purposes in this article because it happens that the average of percent daily returns over this period is very close to zero for both ETNs. You can see that in Figure 4 below, which shows running totals for the percent daily returns for the indexes of both ETNs. Running totals for each end at zero, which of course means that the average percent daily return was also zero. Click to enlarge Figure 4. Running total of daily percent changes. The concept of variance drain was introduced by Tom Messmore in the context of comparing investment advisors based on average yearly percent returns. In brief, average periodic returns is a mathematically incorrect basis for comparison, since percentage gains accrue multiplicatively, not additively. This is best explained by example. Suppose you invest $100 in asset X. On Day 1, its market value falls by 25%. However, on Day 2, it rises by 25%. The average daily rate of return is (-25% + 25%)/2 = 0%. But your investment has not returned to its original value. Instead, it is now worth: $100*(1-0.25)*(1+0.25) = $93.75 A 6.25% loss. Since multiplication is commutative, order doesn’t matter. Investment Y that performs inversely to investment X, gaining 25% on Day 1, then losing 25% on Day 2 will also lose 6.25%. In general, this can be expressed as: I 0 *(1-α)*(1+α) = I 0 -α 2 , where I 0 is the initial investment. Clearly, the larger α is, the greater the net loss. Note that variance drain is not an actual loss. There’s no counterparty to variance drain. Nor is it a frictional drag in the sense that fees or leverage cost are. Rather it’s a demonstration that average periodic returns do not represent longer-term returns over multiple periods. In the case of the volatility ETNs XIV and VXX, the inverse relationship of their daily percent returns simply does not carry over to longer time periods, except by chance. What this means is that the question of why both XIV and VXX lost value, which several readers have raised in the comment sections of recently published articles on the volatility ETNs, is only a question if one starts from an incorrect assumption – namely that XIV and VXX are inversely correlated over time periods longer than one day. Since they’re not, both may lose value over time. Additionally, during time periods longer than one day when one loses as the other gains, those changes should not be expected to be equal and opposite. It’s also worth noting that excess of contango during this approximately one-year period did not result in XIV gaining value. On the contrary, it lost a substantial amount of its prior value. I’d like to encourage those who trade these ETNs to be certain the risks are well understood. Among those risks is the risk of placing too much faith in axioms and strategies that were formed during a period when the VIX was generally calm and declining. They may not apply during prolonged periods when the VIX is rising or is more frequently spiking. Disclosure: I am/we are long XIV. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article. Additional disclosure: I may initiate or close a long or short position in any of the volatility ETNs over the next 72 hours.

The Wisdom Of Twitter Crowds: Tweet-Based Asset-Allocation Strategy Outperforms Several Benchmarks

By Jacob Wolinsky Interesting study and finding from Andrew Lo re Twitter and FOMC: “The Wisdom of Twitter Crowds: Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds” Pablo D. Azar is a PhD student in the Department of Economics and Laboratory for Financial Engineering, Sloan School of Management, MIT. Email: pazar@mit.edu Andrew W. Lo is Charles E. and Susan T. Harris Professor and the Director of the Laboratory for Financial Engineering, Sloan School of Management, MIT. Email: alo-admin@mit.edu Abstract With the rise of social media, investors have a new tool to measure sentiment in real time. However, the nature of these sources of data raises serious questions about its quality. Since anyone on social media can participate in a conversation about markets—whether they are informed or not—it is possible that this data may have very little information about future asset prices. In this paper, we show that this is not the case by analyzing a recurring event that has a high impact on asset prices: Federal Open Market Committee (FOMC) meetings. We exploit a new dataset of tweets referencing the Federal Reserve and show that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors. To gauge the economic magnitude of these predictions, the authors construct a simple hypothetical trading strategy based on this data. They find that a tweet based asset-allocation strategy outperforms several benchmarks, including a strategy that buys and holds a market index as well as a comparable dynamic asset allocation strategy that does not use Twitter information. Investor sentiment has frequently been considered an important factor in determining asset prices. Traditionally, sentiment is measured by observing analyst estimates, survey data, news stories, and technical indicators such as put/call ratios and relative strength indicators. Two drawbacks of these indicators are that they are based on a relatively sparse subset of the population of investors and, except for technical indicators, are not measured in real time. The rise of social media allows us to overcome these drawbacks and measure the sentiment of a large number of individuals in real time. These data sources give the quantitative investor a new tool with which to construct portfolios and manage risk. However, because social media data is generated by individual users and not investment professionals, the following questions arise about the quality of this data: • Do user messages contain relevant information for asset pricing? • Can this information be inferred from more traditional sources, or is it truly new information? • Can social media data help predict future asset returns and shifts in volatility? To answer these questions, we focus on a single recurring event that reveals previously unknown information to the market: Federal Open Market Committee (FOMC) meetings. Eight times a year, the FOMC meets to determine monetary policy. The decisions made by the FOMC are highly watched by all market participants, and often have a significant impact on asset prices.1 To understand how investors on social media behave around FOMC meeting dates, we create a new dataset of tweets that cite the Federal Reserve. Using natural language processing techniques, we can assign a polarity score to each Twitter message, identifying the emotion in the text. We show that this polarity score can be used to predict the returns of the CRSP Value-Weighted Index, even when limiting ourselves to articles and tweets that are published at least 24 hours before the FOMC meeting. We use these results to construct trading strategies that bet more or less aggressively in a market index depending on Twitter sentiment. We find that portfolios using Twitter data can significantly outperform a passive buy-and-hold strategy. Click to enlarge Click to enlarge Full study below SSRN-id2756815