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On Contango-Based XIV Trading Strategies

Summary In July 2014, Seeking Alpha author Nathan Buehler discussed a strategy where you short VXX when VIX goes from backwardation to contango, and cover when VIX re-enters backwardation. Buying XIV rather than shorting VXX is a very similar idea. The XIV version of Mr. Buehler’s strategy can be viewed as making a 1-day bet on XIV whenever VIX is in contango. VIX contango is a useful predictor of 1-day XIV growth. But historically a contango cut-point around 5% rather than 0% generates better raw and risk-adjusted returns. XIV is extremely risky (beta > 4), but trading strategies based on VIX contango appear promising. Background The VelocityShares Daily Inverse VIX Short-Term ETN (NASDAQ: XIV ) has had tremendous growth since it was introduced in late 2010, but has suffered major losses recently. (click to enlarge) The recent 11.9% dip in the SPDR S&P 500 Trust ETF (NYSEARCA: SPY ) coincided with XIV losses of 55.7%. XIV is still ahead of SPY since inception by a fair amount ($26.2k vs. $18.0k), but the extreme volatility of XIV makes it arguably an inferior investment (Sharpe ratio = 0.040 for XIV, 0.055 for SPY). In my view, XIV is a rather dubious fund to buy and hold long-term. It amplifies returns, but seems to amplify volatility even more, resulting in worse risk-adjusted returns than SPY. But trading XIV based on VIX contango – that is, the percent difference between the first and second month VIX futures prices (available at vixcentral.com ) – appears very promising. The purpose of this article is to assess the predictive value of VIX contango, and to assess and attempt to improve a strategy proposed by Seeking Alpha author Nathan Buehler. Data Source and Methods I obtained daily VIX contango/backwardation data and historical XIV and SPY prices from The Intelligent Investor Blog . Daily contango/backwardation is defined as the percent difference between the first and second month VIX futures. While the Intelligent Investor dataset includes simulated XIV data going back to 2004, for this article I only use the actual daily closing prices for XIV since its inception in Nov. 2010. I used R (“quantmod” and “stocks” packages) to analyze data and generate figures for this article. A Look at Nathan Buehler’s Strategy In the Seeking Alpha article Contango and Backwardation Strategy for VIX ETFs , Mr. Buehler suggests shorting VXX when VIX goes from backwardation to contango, and closing the position when VIX re-enters backwardation. The exact time frame for back-testing is a little unclear to me, but Mr. Buehler reported 221.09% total growth from ten VXX trades between May 21, 2012, and April 14, 2014. That is impressive growth. Then again, VXX fell 86.1% over this time period, and XIV gained 213.9%. So it’s a bit unclear how much of the strong performance was due to VXX tanking over the entire time period, and how much was due to the contango strategy providing good entry and exit points. I am not a short seller so I’m more interested in the “buy XIV” version of Mr. Buehler’s strategy. Let’s consider an approach where you look at VIX contango at the end of each trading day. If VIX has entered contango, you buy XIV; if it has entered backwardation, you sell XIV. If we backtest this strategy since XIV’s inception, ignoring trading costs, we get the following performance: (click to enlarge) The contango-based XIV strategy performs well relative to buying and holding XIV for the entire period, achieving a higher final balance ($57.0k vs. $26.2k), smaller maximum drawdown (56.3% vs. 74.4%), and a better Sharpe ratio (0.061 vs. 0.040). Looking at the graph, we see a major divergence in mid-2011 when selling XIV avoided a huge loss. However, there were many times where the contango strategy failed to prevent big losses. Note that buying XIV when VIX enters contango, and selling when it enters backwardation, is equivalent to holding XIV for 1 day whenever VIX is in contango. So this strategy is entirely dependent on VIX contango predicting 1-day XIV growth. VIX Contango and 1-Day XIV Growth For Mr. Buehler’s strategy to have worked so well over the past 5 years, there must have been positive correlation between VIX contango and subsequent 1-day XIV growth. There was indeed some correlation, but not very much. (click to enlarge) The Pearson correlation was 0.059 (p = 0.04), and the Spearman correlation 0.027 (p = 0.35). Note that VIX contango explained only 0.3% of the variability in subsequent 1-day XIV growth. But there does appear to be some predictive value in VIX contango. It’s a little easier to see when you filter out some of the noise and look at mean 1-day XIV growth across quartiles of VIX contango. (click to enlarge) Naturally, we’d hope that VIX contango has enough predictive power to pull the distribution of XIV gains a little bit in our favor. The next figure compares the distribution of XIV gains on days after VIX ended in contango to days after it ended in backwardation. (click to enlarge) The mean was higher for contango vs. backwardation, but the difference was not statistically significant (0.22% vs. -0.26%, t-test p = 0.37). Surprisingly the median was a bit higher for backwardation (0.50% vs. 0.86%, Wilcoxon signed-rank p = 0.62). Towards A Better Cut-Point Holding XIV whenever VIX is in contango is somewhat natural, but there’s no reason we have to use 0% as our cut-point. We might do better if we hold XIV when VIX is in contango of at least 5%, or at least 10%, or some other cut-point. Actually if you look at the regression line in the third figure, you can work out that the expected 1-day XIV growth is only positive for VIX contango of 1.65% or greater. Based on that, we actually wouldn’t want to hold XIV when contango is betwen 0% and 1.65%. Let’s compare 0%, 5%, and 10% VIX contango cut-points. (click to enlarge) The higher cut-point you use, the less frequent your opportunities to trade XIV, but the better the trades tend to be. Notice how the 10% cut-point rarely allows for trades, but tends to climb really nicely when it does. Performance metrics for XIV and the three contango-based XIV strategies are summarized below. Performance metrics for XIV and XIV trading strategies with various VIX contango cut-points. Fund Growth of $10k MDD Overall Sharpe Ratio Sharpe Ratio for Trades XIV $26.2k 74.4% 0.040 0.040 Contango > 0% $57.0k 56.3% 0.061 0.065 Contango > 5% $65.1k 37.3% 0.072 0.090 Contango > 10% $49.3k 14.9% 0.110 0.293 Total growth was best for a contango cut-point of 5%, while maximum drawdown decreased and Sharpe Ratio increased with increasing contango cut-point. (Note that “overall Sharpe ratio” includes the 0% gains on non-trading days, while “Sharpe ratio for trades” does not.) Of course we aren’t restricted to cut-points in 5% intervals here. Let’s play a maximization game and see what VIX contango cut-point would have been optimal for total growth and for overall Sharpe ratio. (click to enlarge) Final balance peaks at VIX contango in the 5-6% range, and is maximized at $100.4k for VIX contango of 5.42%. Overall Sharpe ratio is maximized at 0.115 for VIX contango of 9.95%. Sharpe ratio for trades is maximized at 4.231 for VIX contango at the highest possible value, 21.6%. Of course it wouldn’t make much sense to use a cut-point of 21.6%, as that number is hardly ever reached. Play Both Sides of the Trade? If sufficient VIX contango favors holding XIV, it seems that sufficient VIX backwardation would favor holding VXX. That brings to mind a trading strategy where you buy XIV when VIX contango reaches a certain value, and buy VXX when VIX backwardation reaches a certain value. Trading both XIV and VXX would provide more opportunities for growth. Indeed many of the analyses presented so far are similar when you look at holding VXX based on VIX backwardation. In particular: VIX backwardation is positively correlated with 1-day VXX growth. Regression analysis suggests that VXX on average grows when VIX backwardation is at least 0.38% (equivalently, VIX contango is -0.38% or more negative). Growth of $10k for a backwardation-based VXX strategy is maximized at $13.3k, when you hold VXX when VIX backwardation is at least 5.67%. Unfortunately, 33% growth over 5 years with VXX is nothing compared to 900+% growth with XIV. I experimented with strategies that use both XIV and VXX, but was unable to improve upon XIV-only strategies. Concerns One of my concerns with these strategies is that we’re working with a very weak signal. VIX contango explains about one-third of one percent of XIV’s growth the next day. Contango-based volatility trading strategies do appear to have potential, but keep in mind that VIX contango just isn’t a strong predictor of XIV growth. Another concern is that the excellent historical performance of these strategies may be driven by the bull market of the past 5 years. I think it is very possible that in a bear market these strategies might work poorly for XIV, and perhaps well for VXX. Each strategy involves holding XIV/VXX at certain time intervals, so of course they will be affected by the underlying drift of XIV/VXX. After all, the absolute best you can do with either version of the trade is the total upswing in the fund you are trading over a period of time. Finally, I have noticed in the past that XIV seems to have positive alpha when markets are strong, and negative alpha when markets are weak. This makes it really hard to do portfolio optimization, as the net alpha of a weighted combination of funds including XIV actually depends on what sort of market you’re in. I think an analogous problem could arise for contango-based XIV strategies. For example, holding XIV when VIX contango is at least 5% may only be prudent in periods when XIV itself is rapidly growing, which would typically occur in a strong market. And a strategy that only works during bull markets isn’t very exciting. Conclusions A variant of a strategy discussed by Nathan Buehler, where you hold XIV whenever VIX is in contango, appears promising based on backtested data since Nov. 2010. But increasing the contango cut-point from 0% to 5% increases total returns while also improving Sharpe ratio and reducing MDD. Going to 10% further improves the Sharpe ratio and reduces MDD, but sacrifices total growth as there are fewer trading opportunities. Since Mr. Buehler’s strategy is based on the idea that VIX contango favors XIV, increasing the contango cut-point above 0% makes a lot of sense. It allows us to trade XIV only when we have a substantial advantage due to contango, which reduces trading frequency and therefore trading costs. Strategies based on backtested data are almost always overly optimistic, and I suspect that this analysis is no exception. I am particularly concerned that much of the excellent historical performance is due to XIV’s positive alpha during the past 5 years, which itself was due to a strong market. Therefore, I probably wouldn’t recommend implementing these strategies just yet, at least not with much of your portfolio. Personally, I would consider freeing up a small portion of my portfolio for occasional high-conviction XIV trades based on VIX contango. For example, I might buy XIV on the relatively rare occasion that VIX contango reaches 10%.

Building A Black Swan-Proof Portfolio

Summary The Volkswagen emissions debacle exemplifies the unpredictable risks (“black swans”) associated with investing in even blue-chip stocks. Avoiding companies with high carbon emissions, as suggested by one author, won’t protect us against the next black swan. For that, we need a black swan-proof portfolio. We note two ways of building a black swan-proof portfolio, detail one of them, and provide an example black swan-proof portfolio. Anticipating The Black Swan Working in the mutual fund industry in the late 1990s, I sat through a number of presentations by fund company economists. They often had question-and-answer sessions, and I’ve forgotten about most of them. But one particular incident stayed with me, as the fund company economist touched on an idea Nassim Nicholas Taleb would later popularize in his 2007 book The Black Swan . The year was in 1999, and if memory serves, the economist was Dr. Bob Froehlich . An investor asked him if we should be worried about Y2K , the widely-anticipated “Year 2000 Problem”, when computer systems programmed to use two digits to record years might get confused by the switchover from “99” to “00”. The economist answered that he wasn’t worried about Y2K, because the electronic debut of the euro as the EU’s currency earlier that year had been a similarly challenging computer problem, and it went smoothly. He then offered a Black Swan-like admonition: If you’ve been hearing a lot about a problem in the news, that means experts are already working on it, so you don’t need to worry about it. Worry about what you haven’t been hearing about. Two Types Of Black Swans Black swans are the crises that you don’t hear about in the news beforehand, and, broadly speaking, there are two types of them: systemic black swans, and stock-specific black swans. An example of a systemic black swan was the freezing of the credit markets during the credit crisis, which affected many companies. An example of a stock-specific black swan is the emissions scandal at Volkswagen ( OTCQX:VLKAY ), which was the subject of John Authers’ “Long View” column in the Financial Times (“Carbon footprints loom for investors after VW scandal”) last weekend. From Blue Chip to Black Swan (click to enlarge) Volkswagen, the blue chip automaker, had once praised itself for its putatively low-emission diesel vehicles by having its engineers sprout angelic wings in an ad campaign, as pictured above (image via this New York Times article on the scandal). In his column, John Authers argued that the VW scandal was a rare case in which the appellation “black swan” was warranted: The phrase “black swan” – meaning an unprecedented low-probability event that prompts markets to overreact – tends to be overused. People will invoke it when really they have simply failed to hedge adequately against obvious risks. But Volkswagen, the German carmaker, produced a true “black swan” this week, as it was revealed that it had for years used complicated software that allowed its diesel-fuelled cars to “cheat” on emissions tests. Drawing The Wrong Lesson Authers went on to suggest that investors use data from MSCI and other index providers to lower their exposure to companies with large carbon footprints. With all due respect to Authers, that’s the wrong lesson to draw from this disaster for Volkswagen shareholders. Authers’ advice is an example of checklist investing, and as we pointed out in a recent article (“A Checklist To Save Your Assets”), those sorts of checklists don’t limit risk. In that article, we recounted the history of a hedge fund manager who developed a 98-question checklist to reduce his error rate, and nevertheless added to a concentrated position in Horsehead Holding Corp. (NASDAQ: ZINC ) at over $12 per share in 2013, and continues to be the largest institutional holder of that stock, which closed under $4 per share on Friday. (click to enlarge) We then noted: Like the margin of safety concept, 98-question checklists may be helpful for security selection. They just don’t limit either of the two kinds of risk associated with stock investing: idiosyncratic risk , the risk of something bad happening to one of the companies you own, and market risk , the risk of your investments suffering due to a decline of the market as a whole. Faulty carbon emissions are in the news now, which means experts are already working to resolve the issue; we need to worry about the next black swan. Of course, by definition, we don’t know what the next black swan will be, or where or when it will strike. But, fortunately, we can build a black swan-proof portfolio without knowing the answers to those questions. Two Ways Of Building A Black Swan-Proof Portfolio A black swan-proof portfolio is one in which both your stock-specific risk and your systemic or market risk are strictly limited. There are two ways to construct one: Use diversification to limit your stock-specific risk, and then use other methods to limit your market risk in according with your risk tolerance. Hedge each position in your portfolio to limit your stock-specific and market risk according to your risk tolerance. In this post, we’ll detail the second method. The beauty of the second method of building a black swan-proof portfolio is that it doesn’t matter what the black swan ends up being: whether it’s financial crisis or a meteor hitting a company’s headquarters, we’re not hedging against a specific event, but the effect of any event on the share price. Whatever happens, our downside will be strictly limited. The hedged portfolio method offers a way to build a black swan-proof portfolio while maximizing your expected return. Below, we’ll run through the process of creating a black swan-proof portfolio using this method, and provide an example using an automated tool. First, we need to note the tradeoff between risk tolerance and expected return. Risk Tolerance, Hedging Cost, And Expected Return All else equal, with a hedged portfolio, the greater an investor’s risk tolerance – the greater the maximum drawdown he is willing to risk (his “threshold”) – the higher his expected return will be. So, for example, an investor willing to risk a decline of 25% would likely have a higher expected return than one willing to risk a decline of only 15%. We’ll split the difference below, and construct a hedged portfolio for an investor who is willing to risk a decline of no more than 20%, and has $500,000 to invest. Constructing A Hedged, Or Black Swan-Proof Portfolio The process, in broad strokes, is this: Find securities with high potential returns (we define potential return as a high-end, bullish estimate of how the security will perform). Find securities that are relatively inexpensive to hedge. Buy a handful of securities that score well on the first two criteria; in other words, buy a handful of securities with high potential returns net of their hedging costs (or, ones with high net potential returns). Hedge them. The potential benefits of this approach are twofold: If you are successful at the first step (finding securities with high potential returns), and you hold a concentrated portfolio of them, your portfolios should generate decent returns over time. If you are hedged, and your return estimates are completely wrong, on occasion – or the market moves against you – your downside will be strictly limited. How To Implement This Approach Finding securities with high potential returns For this, you can use Seeking Alpha Pro, among other sources. Seeking Alpha articles often include price targets for long ideas, and you can convert these to percentage returns from current prices. But you’ll need to use the same time frame for each of your expected return calculations to facilitate comparisons of expected returns, hedging costs, and net expected returns. Our method starts with calculations of six-month potential returns. Finding Securities That Are Relatively Inexpensive To Hedge For this step, you’ll need to find hedges for the securities with high potential returns, and then calculate the hedging cost as a percentage of position value for each security. Whatever hedging method you use, for this example, you’d want to make sure that each security is hedged against a greater-than-18% decline over the time frame covered by your potential return calculations. Our method attempts to find optimal static hedges using collars as well as protective puts. Buying Securities That Score Well On The First Two Criteria To determine which securities these are, you may need to first adjust your potential return calculations by the time frame of your hedges. For example, although our method initially calculates six-month potential returns and aims to find hedges with six months to expiration, in some cases the closest hedge expiration may be five months out. In those cases, we will adjust our potential return calculation down accordingly, because we expect an investor will exit the position shortly before the hedge expires (in general, our method and calculations are based on the assumption that an investor will hold his shares for six months, until shortly before their hedges expire or until they are called away, whichever comes first). Next, you’ll need to subtract the hedging costs you calculated in the previous step from the potential returns you calculated for each position, and sort the securities by their potential returns net of hedging costs, or net potential returns. The securities that come to the top of that sort are the ones you’ll want to consider for your portfolio. Fine-Tuning Portfolio Construction You’ll want to stick with round lots (numbers of shares divisible by 100) to minimize hedging costs. Another fine-tuning step is to minimize cash that’s left over after you make your initial allocation to round lots of securities and their respective hedges. Because each security is hedged, you won’t need a large cash position to reduce risk. And since returns on cash are so low now, by minimizing cash, you can potentially boost returns. In this step, our method searches for what we call a “cash substitute”: that’s a security collared with a tight cap (1% or the current yield on a leading money market fund, whichever is higher) in an attempt to capture a better-than-cash return while keeping the investor’s downside limited according to his specifications. You could use a similar approach, or you could simply allocate leftover cash to one of the securities you selected in the previous step. Calculating An Expected Return While net potential returns are bullish estimates of how well securities will perform, net of their hedging costs, expected returns, in our terminology, are the more likely returns net of hedging costs. In a series of 25,412 backtests over an 11-year time period, we determined two things about our method of calculating potential returns: it generates alpha, and it overstates actual returns. The average actual return over the next six months in those 25,412 tests was 0.3x the average potential return calculated ahead of time. So, we use that empirically derived relationship to calculate our expected returns. An Automated Approach Here, we’ll show an example of creating a black swan-proof portfolio using the general process described above, facilitated by the automated hedged portfolio construction tool at Portfolio Armor. In the first field below, we’re given the choice of entering our own ticker symbols. Instead, we’ll leave that field blank, and let the site pick its own securities for us. In the second field, we enter the dollar amount of our investor’s portfolio (500,000), and in the third field, the maximum decline he’s willing to risk in percentage terms (20). Next, we clicked the “create” button. A couple of minutes later, we were presented with the hedged portfolio below. The data here is as of Friday’s close: Why These Particular Securities? Portfolio Armor looks at two factors to estimate potential returns: price history, and option market sentiment. Then, it subtracts hedging costs to calculate potential returns net of hedging costs, or net potential returns. The securities included in this portfolio had some of the highest net potential returns in Portfolio Armor’s universe on Friday. Let’s turn our attention now to the portfolio level summary. Worst-Case Scenario The “Max Drawdown” column in the portfolio level summary shows the worst-case scenario for this hedged portfolio. If every underlying security in it went to zero before the hedges expired, the portfolio would decline 19.39%. Negative Hedging Cost Note that, in this case, the total hedging cost for the portfolio was negative, -0.63%, meaning the investor would receive more income in total from selling the call legs of the collars on his positions than he spent buying the puts. Best-Case Scenario At the portfolio level, the net potential return is 17.27%. This represents the best-case scenario if each underlying security in the portfolio meets or exceeds its potential return. A More Likely Scenario The portfolio level expected return of 6.62% represents a more conservative estimate, based on the historical relationship between our calculated potential returns and actual returns. Each Security Is Hedged Note that in the portfolio above, each underlying security is hedged. Amazon.com (NASDAQ: AMZN ), BofI Holding (NASDAQ: BOFI ), Netflix (NASDAQ: NFLX ), Skechers USA (NYSE: SKX ), Tyler Technologies (NYSE: TYL ), and Under Armour (NYSE: UA ) are hedged with optimal collars with their caps set at their respective potential returns. Celgene (NASDAQ: CELG ) is hedged as a cash substitute, with an optimal collar with its cap set at 1%. Hedging each security according to the investor’s risk tolerance obviates the need for broad diversification, and lets him concentrate his assets in a handful of securities with high potential returns net of their hedging costs. Here’s a closer look at the hedge for one of these positions, UA: As you can see in first part of the image above, UA is hedged with an optimal collar with its cap set at 19.08%, which was the potential return Portfolio Armor calculated for the stock: the idea is to capture the potential return while offsetting the cost of hedging by selling other investors the right to buy UA if it appreciates beyond that over the next six months. The cost of the put leg of this collar was $2,580, or 4.15% of position value, but, as you can see in the image below, the income from the short call leg was $2,100, or 3.37% as percentage of position value. Since the income from the call leg offset some of the cost of the put leg, the net cost of the optimal collar on UA was $480, or 0.77% of position value.[i] Note that, although the cost of the hedge on this position was positive, the hedging cost of this portfolio as a whole was negative . Possibly More Protection Than Promised In some cases, hedges such as the ones in the portfolio above can provide more protection than promised. For an example of that, see this recent instablog post on hedging Tesla (NASDAQ: TSLA ). Hedged Portfolios For More Risk-Averse Investors The hedged portfolio shown above was designed for an investor who could tolerate a decline of as much as 20% over the next six months, but the same process can be used for investors who are even more risk-averse, willing to risk drawdowns of as little as 2%. Notes: [i] To be conservative, the net cost of the collar was calculated using the bid price of the calls and the ask price of the puts. In practice, an investor can often sell the calls for a higher price (some price between the bid and ask) and he can often buy the puts for less than the ask price (again, at some price between the bid and ask). So, in practice, the cost of this collar would likely have been lower. The same is true of the other hedges in this portfolio, the costs of which were also calculated conservatively. Editor’s Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks.

CEFL’s Closed-End Funds’ Discounts To Book Value Defy Logic

The closed-end funds that comprise the index upon which CEFL is based are now trading at an average 13.8% discount to book value. This discount is beyond normal ranges and logically cannot be a function of market expectations. CEFL is projected to pay a monthly dividend of $0.3074, which brings the annualized monthly compounded yield to 23.7%. The high yield and discount to book value make a compelling case for CEFL. All 30 of the index components of the UBS ETRACS Monthly Pay 2xLeveraged Closed-End Fund ETN (NYSEARCA: CEFL ), and the YieldShares High Income ETF (NYSEARCA: YYY ), which is based on the same index and thus has the same components as CEFL, but without the 2X leverage, are now trading at discounts to book value. This is the first month since the inception of CEFL that this has been the case. Last month, two of the components were trading at premiums to book value. On a weighted average basis, the closed-end funds that comprise CEFL are trading at a 13.8% discount to book value as of September 18, 2015. The median discount for the 30 closed-end funds is 14.25%. Closed-end funds typically trade at either discounts or premiums to book value. On balance, there is a slight bias towards discounts. Because of significant changes in the composition of the index, comparisons of aggregate discounts to book value from previous years are not very meaningful. That said, the 13.8% discount is the largest since the inception of CEFL. The 13.8% weighted average discount to book value of the components that comprise the index is an increase in the discount that I computed last month of 13.1%. Five months ago, CEFL had an 8.6% weighted discount to book value. Thus, in just five months, the discount has increased from 8.6% to 13.8%. For many securities other than closed-end funds, such as common stocks, discounts or premiums to book values are logically based on the business prospects for companies. Thus, Google (NASDAQ: GOOG ) (NASDAQ: GOOGL ) trades at significant premium to book value while Peabody Energy (NYSE: BTU ) trades at a significant discount to book value, reflecting differing market perceptions of the future prospects for those companies. Google trades at approximately 5X book value while BTU trades at about 1/5 of book value. In my article: mREITs Impacted By Enormous Price To Book Swing – MORL Yielding 27.6% , I discussed the large discounts to book value that mREITs such as American Capital Agency Corp. (NASDAQ: AGNC ) are trading at. The logic behind mREITs such as AGNC trading at significant discounts to book value is primarily based on the possible impacts of higher future interest rates. Whether one agrees or disagrees with the magnitudes of the discounts or premiums to book for securities such as Google, Peabody and American Capital Agency, there are facts and logic related to each company’s business prospects that could possibly explain or justify changes in the premiums or discounts that have occurred in those stocks. There are no such facts or changes in market forecasts of business prospects that can possibly explain or justify changes in the premiums or discounts that have occurred in the closed-end funds that comprise CEFL. For closed-end funds, changes in the premiums or discounts to book value should be solely based on the value that investors place on the relative advantages and disadvantages of the closed-end fund structure, rather than the differing market perceptions of the future prospects for the securities in the closed-end funds’ portfolios. Investors in closed-end funds could purchase the securities held by a closed-end fund themselves. In most cases, there are also open-end funds available to investors that have risk, return and expense characteristics similar to any given closed-end fund. Changes in market perceptions of the prospects of the securities that comprise the portfolios of closed-end funds cannot logically explain or justify any change in the magnitudes of the discounts or premiums to book for the closed-end funds. Any such changes in market perceptions of the prospects of the securities in the portfolio should be reflected in the prices of the portfolio securities themselves. Thus, the ratio of the price of the closed-end fund to its book value should not be related to the expectations of the prospects for the portfolio securities held by the closed-end fund. If investors value the advantages of diversification, management and possibly lower transaction costs associated with owning a closed-end fund rather than owning the individual securities that comprise the closed-end fund’s portfolio more than the fees and expenses which are the primary negative aspect of closed-end funds, then the closed-end fund will trade at a premium to book value. Conversely, if investors feel that the fees and expenses of the closed-end fund outweigh the advantages of diversification, management and possibly lower transaction cost associated with owning a closed-end fund, it will trade at a discount to book value. The trade-offs between the advantages and disadvantages associated with closed-end funds relative to the securities that comprise the portfolios of the closed-end funds are rational reasons for the closed-end funds to trade at discounts or premiums to book value. However, it is not rational for the discount or premium to be influenced by expectations of future returns on the securities that comprise the portfolios of the closed-end funds. If the market thinks that the securities in a closed-end fund’s portfolio will decline, and thus the net asset or book value of the closed-end fund will decline, there is no reason why the premium or discount that the closed-end fund is trading at should change. Some closed-end funds employ limited amounts of leverage. As investment companies, closed-end funds cannot have more than 33% leverage and most employ less, if any. That a closed-end fund does or does not employ a relatively small amount of leverage should not impact the premium or discount that the closed-end fund is trading at. Leverage is the easiest characteristic of a security to offset. Thus, if an investor was interested in a security but did not like the fact that the security employed 20% leverage, the investor could offset that leverage by combing that security with a risk-free asset. For example, if you had $10,000 to invest and you liked a closed-end fund but were unhappy with the 20% leverage, investing $8,000 in the closed-end fund and $2,000 in a risk-free asset will result in the same risk/return profile as investing $10,000 in the same closed-end fund, if that fund did not employ any leverage. Likewise, if you liked a closed-end fund but would rather that fund employed more leverage, you can buy that fund on margin and get in the same risk/return profile as investing in the fund if it had more leverage. Thus, leverage or lack of leverage should not influence the premium or discount that the closed-end fund is trading at since any leverage in a closed-end fund can be offset by an investor. There should be some limits as to how far away from book value a closed-end fund should trade. If a closed-end fund is trading at a sufficiently high premium to book value, an arbitrage opportunity could exist. Buying the securities in the closed-end fund’s portfolio and simultaneously selling the closed-end fund should generate a profitable arbitrage. Likewise if a closed-end fund is trading at a large enough discount buying the closed-end fund and selling the securities that comprise the portfolio, it could generate arbitrage profits. These types of arbitrage would be risk arbitrage as opposed to riskless arbitrage. In riskless arbitrage, one buys a security or commodity and simultaneously sells something that is the equivalent of what you sold. An example of riskless arbitrage would be, after a merger had been approved in which the acquirer is issuing one share of its stock for two shares of the company being acquired, you simultaneously buy two shares of the company being acquired for a total cost less than a share of the acquirer. This would essentially lock in a profit that would be realized when the merger closed and the values converged. Attempting to take advantage of the discount to book value being irrationally wide for a closed-end fund would be an example of risk arbitrage since there is no terminal event which will make the value of what you buy converge with what you sell. It may be irrational for a closed-end fund to trade at a 10% discount to book value. However, there is always the possibility that it could go to a 15% discount as Keynes famously said “The market can stay irrational longer than you can stay solvent.” Closed-end funds do not usually provide convenient opportunities for explicit risk arbitrage transactions where one security is bought and the other security is shorted. Retail investors usually cannot use the proceeds from selling some securities short to buy other securities. Hedge funds and institutions which may be able to use the proceeds from selling some securities short to buy others might find closed-end funds, and especially some of the securities that comprise the portfolios of the closed-end funds, not liquid enough to trade in. Even, market participants who are able to use the proceeds from selling some securities short to buy others might be dissuaded from buying closed-end funds and shorting the securities in the closed-end funds’ portfolio, because of the fees and expenses charged by the closed-end funds. However, if the discount to book value is large enough, the fees and expenses charged by the closed-end funds could be offset by the discount to book value and thus generate a positive carry for a long closed-end fund – short the fund’s portfolio position. This would be especially true for closed-end funds that specialize in securities that generate higher income, such as those in the index upon which CEFL and its unleveraged counterpart YYY are based. An example of the discount to book value more than offsetting the fees and expenses would be a hypothetical closed-end fund whose portfolio securities yielded 10% before expenses. Most income-oriented closed-end funds have expense ratios lower than 1%. Shorting $100 worth of the securities that comprise the fund would require payments of $10 representing 10% annually to those who the securities were borrowed from. The $100 proceeds from the short sale could be used to acquire $100 of the closed-end fund. If the closed-end fund was trading at a 14% discount, $100 of the fund would represent 100/.86 = $116.28 worth of the securities in the fund. These securities yield 10%, so the gross income from the fund position would be $11.63. The net income, assuming a 1% expense ratio, would be $10.63. Thus, even after expenses and fees, an account long the closed-end fund would generate higher income than the portfolio securities while it waited for the discount to narrow to realize the risk arbitrage profit. While explicit risk arbitrage where the portfolio securities are shorted and the proceeds are employed to buy the closed-end fund might not occur in significant quantities to narrow the discount to book value, implicit arbitrage should eventually have an impact. Implicit risk arbitrage would occur as investors holding or wanting to hold securities with similar risk/return characteristics as a closed-end fund or the portfolios held by the closed-end fund shift from other securities to the closed-end fund. Institutional investors that had portfolios which contained securities similar to or identical to those held in a close-end fund could improve their risk/return profile by shifting out of securities in the closed-end fund to the closed-end fund, if the discount to book value for the closed-end fund was large enough. Retail investors could switch from securities held in portfolios of close-end fund to the closed-end fund and improve their risk/return profile if the discount to book value for the closed-end fund was large enough. More important, investors could shift out open-end mutual funds into closed-end mutual funds with similar objectives and portfolios. Open-end mutual funds are sold and redeemed at net asset value. Thus, there is never any discount or premium to book value for an open-end mutual fund. Advantages for investors in no-load mutual funds are that there are no transactions costs and the funds can always be redeemed at net asset or book value. Closed-end funds usually require some brokerage commission to buy and sell them, and there is risk that the closed-end fund will fluctuate due to changes in the premium or discount to net asset value in addition to fluctuation in the portfolio securities. The advantages of no-load open-end mutual funds are somewhat offset by the lower fees and expenses that closed-end funds usually have. When closed-end funds are trading at large discounts to book value, investors can significantly increase their returns by switching from open-end funds to closed-end funds that have similar assets but are selling at discounts to net asset value and typically have lower fees and expenses. When an investor redeems an open-end fund at net asset value, the open-end fund sells portfolio securities to fund the redemption. That would tend to lower the market prices of those portfolio securities. If the investor uses the proceeds from the redemption of the open-end fund to buy shares in a closed-end fund that holds similar portfolio securities, the net effect would be to put downward pressure on the market prices of the portfolio securities and upward pressure of the market prices of the closed-end funds. Thus, the discount to book value for the closed-end funds will tend to decline. This large discount to net asset value alone is a good reason to be constructive on CEFL. If the discount to book value for the closed-end funds that comprise CEFL were to revert from the current 13.8% to the 8.6% level of five months ago, CEFL would increase in price by 10.2% even if the prices of all of the component closed-end funds remained exactly the same. It should be noted that saying CEFL components are now trading at a deeper discount to the net asset value of the closed-end funds that comprise the index does not mean that CEFL does not always trade at a level close to its own net asset value. Since CEFL is exchangeable at the holders’ option at indicative or net asset value, its market price will not deviate significantly from the net asset value. The net asset value or indicative value of CEFL is determined by the market prices of the closed-end funds that comprise the index upon which CEFL is based. My constructive view on CEFL stems not only from the wide discount to book value of the closed-end funds, but also from the very large dividends paid by CEFL. Of the 30 index components of CEFL, and YYY which is based on the same index and thus has the same components as CEFL, but without the 2X leverage, 29 now pay monthly. Only the Morgan Stanley Emerging Markets Domestic Debt Fund (NYSE: EDD ) now pays quarterly dividends in January, April, October, and July. Thus, EDD will be included in the October 2015 CEFL monthly dividend calculation. My calculation projects an October 2015 dividend of $0.3074. This is an increase of 4.6% from the September 2015 dividend of $0.2938 which did not include any contribution from EDD. A more relevant comparison is to the July 2015 which also included all 30 CEFL components. The projected October 2015 dividend of $0.3074 is a decline of 5.7% from the July 2015 CEFL dividend. The decline in the CEFL monthly dividend compared to July 2015 is primarily due to the reduction in the indicative or net asset value of CEFL. The indicative value of each CEFL share has decreased from $19.1358 on July 31, 2015, to $ 16.8889 on September 18, 2015. As I explained in MORL Dividend Drops Again In October, Now Yielding 21.5% On A Monthly Compounded Basis, if the dividends on all of the underlying components in a 2X leveraged ETN, such as CEFL, were to remain the same for a specific month, but the indicative value (aka net asset value or book value) was lower, the dividend paid, which is essentially a pass-through with no discretion by management, would also decrease. This is the result of the rebalancing of the portfolio each month required to bring the amount of leverage back to 2X. Of course, an increase in indicative value would result in a corresponding increase in the dividend. While the 2014 year-end rebalancing has reduced the monthly CEFL dividend, it is still very large. For the three months ending October 2015, the total projected dividends are $0.9031. The annualized dividends would be $3.61. This is a 21.4% simple annualized yield with CEFL priced at $16.85. On a monthly compounded basis, the effective annualized yield is 23.7%. Aside from the fact that with a yield above 20%, even without reinvesting or compounding, you get back your initial investment in only 5 years and still have your original investment shares intact. If someone thought that over the next five years markets and interest rates would remain relatively stable, and thus CEFL would continue to yield 23.7% on a compounded basis, the return on a strategy of reinvesting all dividends would be enormous. An investment of $100,000 would be worth $289,350 in five years. More interestingly, for those investing for future income, the income from the initial $100,000 would increase from the $23,700 initial annual rate to $68,576 annually. CEFL component weights as of August 28, 2015, prices as of September 18, 2015: Name Ticker Weight Price NAV price/NAV ex-div dividend frequency contribution return of capital First Trust Intermediate Duration Prf. & Income Fd (NYSE: FPF ) 4.81 21.96 23.53 0.9333 9/1/2015 0.1625 m 0.01202 DoubleLine Income Solutions (NYSE: DSL ) 4.54 18.41 20.49 0.8985 9/16/2015 0.15 m 0.01249 Eaton Vance Limited Duration Income Fund (NYSEMKT: EVV ) 4.47 13.03 15.28 0.8527 9/9/2015 0.1017 m 0.01178 MFS Charter Income Trust (NYSE: MCR ) 4.45 8.03 9.42 0.8524 9/15/2015 0.06378 m 0.01194 Eaton Vance Tax-Managed Global Diversified Equity Income Fund (NYSE: EXG ) 4.38 8.97 9.83 0.9125 9/21/2015 0.0813 m 0.01341 0.0676 BlackRock Corporate High Yield Fund (NYSE: HYT ) 4.37 10.26 12 0.8550 9/11/2015 0.07 m 0.01007 0.0012 Clough Global Opportunities Fund (NYSEMKT: GLO ) 4.33 11.07 13.49 0.8206 9/16/2015 0.1 m 0.01321 Alpine Total Dynamic Dividend (NYSE: AOD ) 4.32 7.93 9.56 0.8295 9/21/2015 0.0575 m 0.01058 PIMCO Dynamic Credit Income Fund (NYSE: PCI ) 4.29 18.88 21.98 0.8590 9/9/2015 0.164063 m 0.01259 Alpine Global Premier Properties Fund (NYSE: AWP ) 4.29 5.99 7.16 0.8366 9/21/2015 0.05 m 0.01210 Prudential Global Short Duration High Yield Fund (NYSE: GHY ) 4.24 14.11 16.7 0.8449 9/16/2015 0.11 m 0.01117 Eaton Vance Tax-Managed Diversified Equity Income Fund (NYSE: ETY ) 4.17 10.86 11.86 0.9157 9/21/2015 0.0843 m 0.01093 Western Asset Emerging Markets Debt Fund (NYSE: ESD ) 4.12 13.91 16.86 0.8250 9/16/2015 0.105 m 0.01050 0.0159 Voya Global Equity Dividend & Premium Opportunity Fund (NYSE: IGD ) 4.05 7.27 8.48 0.8573 9/1/2015 0.076 m 0.01430 0.0266 BlackRock International Growth & Income Trust (NYSE: BGY ) 3.83 6.29 7.2 0.8736 9/11/2015 0.049 m 0.01008 0.0490 GAMCO Global Gold Natural Resources & Income Trust (NYSEMKT: GGN ) 3.77 5.24 5.86 0.8942 9/14/2015 0.07 m 0.01701 0.0700 Morgan Stanley Emerging Markets Domestic Debt Fund (EDD) 3.52 7.51 8.99 0.8354 9/26/2015 0.22 q 0.03483 Prudential Short Duration High Yield Fd (NYSE: ISD ) 3.37 14.75 17.17 0.8591 9/16/2015 0.11 m 0.00849 Aberdeen Asia-Pacific Income Fund (NYSEMKT: FAX ) 3.26 4.53 5.47 0.8282 9/16/2015 0.035 m 0.00851 0.0143 MFS Multimarket Income Trust (NYSE: MMT ) 3.02 5.88 6.78 0.8673 9/15/2015 0.04588 m 0.00796 0.0168 Calamos Global Dynamic Income Fund (NASDAQ: CHW ) 2.95 7.42 8.86 0.8375 9/8/2015 0.07 m 0.00940 Backstone/GSO Strategic Credit Fund (NYSE: BGB ) 2.8 14.95 17.43 0.8577 9/21/2015 0.105 m 0.00664 0.0012 BlackRock Multi-Sector Income (NYSE: BIT ) 2.1 16.03 19.01 0.8432 9/11/2015 0.1167 m 0.00516 Western Asset High Income Fund II (NYSE: HIX ) 2.08 6.73 7.72 0.8718 9/16/2015 0.069 m 0.00720 0.0006 AGIC Convertible & Income Fund (NYSE: NCV ) 1.95 6.37 7.21 0.8835 9/9/2015 0.065 m 0.00672 Wells Fargo Advantage Multi-Sector Income Fund (NYSEMKT: ERC ) 1.73 11.76 14.15 0.8311 9/11/2015 0.0967 m 0.00481 0.0283 Wells Fargo Advantage Income Opportunities Fund (NYSEMKT: EAD ) 1.35 7.56 9.04 0.8363 9/11/2015 0.068 m 0.00410 Nuveen Preferred Income Opportunities Fund (NYSE: JPC ) 1.26 9.19 10.23 0.8983 9/11/2015 0.067 m 0.00310 AGIC Convertible & Income Fund II (NYSE: NCZ ) 1.19 5.67 6.42 0.8832 9/9/2015 0.0575 m 0.00408 Invesco Dynamic Credit Opportunities Fund (NYSE: VTA ) 0.97 11 12.79 0.8600 9/10/2015 0.075 m 0.00223