Author Archives: Scalper1

CEFL: A Year In Review, And A Prediction Of What’s Ahead

Summary 2015 has not been a good year for CEFL unitholders: income declined by 20% while price declined by 33%. This article presents a review of CEFL happenings in 2015, and a forecast of what’s ahead for 2016. Based on the publicly available index methodology, the CEFs to be added or removed are predicted. Introduction The ETRACS Monthly Pay 2xLeveraged Closed-End Fund ETN (NYSEARCA: CEFL ) is a 2x leveraged exchange-traded note [ETN] that tracks twice the monthly performance of the ISE High Income Index [symbol YLDA]. The YieldShares High Income ETF (NYSEARCA: YYY ) tracks the same index, but is unleveraged. CEFL is a popular investment vehicle among retail investors due to its high income (24.52% trailing twelve months yield), which is paid monthly. With 2015 nearly behind us, I thought I would review the characteristics of this year’s iteration of CEFL, and also look ahead at what might be in store for us in 2016. (Source: Main Street Investor ) 2015 portfolio YLDA holds 30 closed-end funds [CEFs], and is rebalanced annually. As I have previously discussed in my three-part “X-raying CEFL” series, this year’s iteration of CEFL (and thus also YYY) had the following characteristics: CEFL is comprised of approximately one-third equity and two-thirds debt, is effectively leveraged by 240% and has a total expense ratio of 4.92% per dollar invested in the fund (or 2.05% per dollar of assets controlled) (discussed in ” X-Raying CEFL: Leverage And Expense Ratio Statistics “). CEFL contained around two-thirds of North American (primarily U.S.) assets, with the rest being international. Moreover, the North American component of CEFL contains a higher allocation to debt vs. equity than the European component of CEFL (discussed in ” X-Raying CEFL (Part 2): Geographical Distribution “). CEFL is not very interest-rate sensitive as most of the holdings of CEFL are most-correlated with high-yield debt (discussed in ” X-Raying CEFL (Part 3): Interest Rate Sensitivity “). Actually, I might have been inaccurate in my last prediction. Over the last year, the price action of CEFL has actually moved in the same direction to interest rates, which is exactly opposite to what would be expected for a traditional bond fund. But this is not entirely surprising for CEFL, because high-yield debt usually tend to trade in tandem with equities and in the opposite direction to treasuries. Indeed, CEFL had a positive +0.71 correlation with U.S. equities (via SPDR S&P 500 ETF (NYSEARCA: SPY ) over the past year, but a negative -0.24 correlation with treasuries (via the iShares 20+ Year Treasury Bond ETF) (NYSEARCA: TLT ) (source: InvestSpy ). Thus, readers who worried that higher interest rates would lower the price of CEFL may actually have been pleasantly surprised that the opposite has held true this year. Decreasing yield Seeking Alpha author Professor Lance Brofman has done a wonderful job predicting the upcoming distributions for CEFL (see his latest article here ), while also providing expert commentary in his area of expertise. The distribution history for CEFL, which now has paid out 24 months of dividends, is presented below. Unfortunately, we see that the distributions paid out by CEFL have been in decline. In 2014, each share of CEFL paid out $4.74 of distributions, but in 2015, each share of CEFL only paid out $3.82 of distributions. This means that the distribution of CEFL has declined by 19.5% year on year. I believe that a large reason for the distribution decline can be attributed to the rebalancing debacle that occurred at the turn of this year (see below). CEFL has a current trailing twelve months yield of 24.52%. Rebalancing debacle The annual rebalancing in the index YLDA was disastrous for CEFL and YYY holders. The reasons for this have been summarized in my recent article ” Are You Ready For CEFL’s Year-End Rebalancing ?” In short, up to 10% of the net asset value of CEFL may have been lost due to traders (including, perhaps, UBS themselves) buying and selling the CEFs to be added or removed from the index ahead of the actual rebalancing date (a form of “front-running,” see this Bloomberg article for more information on this phenomenon). For further study on the rebalancing issue, consult my previous articles on this issue in the below links: Predicting the 2016 portfolio How might the portfolio of CEFL change upon the next rebalancing event, which is scheduled to occur in the next few days? As discussed in my most recent CEFL article, the index provider has decided that upcoming index will not be announced 5 days in advance. This was intended to prevent “front-running” of the index. However, with the index methodology published and available to all, I had little doubt that professional investors would be able to use the selection rules to determine which stocks would be added or removed from the index. Therefore, in an attempt to level the playing field for everyone else, I have tried to approximate the index methodology in order to predict CEFL’s portfolio for 2016. The selection methodology for the index is reproduced below (source: ISE ). 1. Restrict selection universe to closed-end funds with market cap > $500M and six month daily average volume > $1M. 2. Rank each fund by the following three criteria: i. Fund yield (descending) ii. Fund share price Premium / Discount to Net Asset Value (ascending) iii. Fund Average Daily Value (ADV) of shares traded (descending) 3. Calculate an overall rank for each fund by taking the weighted average of the three ranks with the following weightings: yield: 50%, premium/discount: 25%, average daily value: 25%. 4. Select the 30 funds with the highest overall rank. Using CEFAnalyzer , I obtained a list of the 141 CEFs with market cap > $500M. Unfortunately, I was unable to apply a volume filter because I was not sure what specific time period CEFAnalyzer reports volume data for. I then replicated the index methodology for the 141 CEFs on this list. The below table shows the top 30 CEFs for either distribution yield or discount among the CEFs with market cap > $500M. Rank Ticker Yield Rank Ticker Discount 1 GGN 17.14% 1 BCX -16.92% 2 PHK 14.58% 2 AOD -16.88% 3 KYN 14.44% 3 AWP -16.29% 4 NHF 14.23% 4 IGR -16.19% 5 HIX 13.06% 5 FAX -16.09% 6 TDF 13.03% 6 RNP -15.64% 7 IGD 12.67% 7 GLO -15.33% 8 RVT 12.40% 8 RVT -15.07% 9 CEM 11.73% 9 NFJ -15.04% 10 PTY 11.69% 10 DPG -15.04% 11 GLO 11.37% 11 UTF -14.93% 12 GAB 11.21% 12 ADX -14.92% 13 EXG 11.17% 13 TY -14.81% 14 BCX 11.12% 14 WIW -14.79% 15 CHI 11.11% 15 TDF -14.63% 16 ETJ 11.05% 16 NXJ -14.58% 17 EAD 10.94% 17 NHF -14.57% 18 DSL 10.89% 18 NIE -13.63% 19 CHY 10.89% 19 NQP -13.40% 20 PFN 10.75% 20 USA -13.32% 21 PCI 10.74% 21 FSD -13.31% 22 FEI 10.44% 22 BIT -13.04% 23 ETW 10.36% 23 GDV -12.81% 24 AWP 10.24% 24 JQC -12.70% 25 NTG 9.95% 25 CAF -12.50% 26 PCN 9.86% 26 IGD -12.41% 27 CSQ 9.81% 27 VTA -12.33% 28 PDI 9.62% 28 RQI -12.27% 29 NFJ 9.62% 29 BDJ -12.10% 30 EVV 9.59% 30 NQU -12.04% The yield ranking was then weighted by 50% while the discount ranking was weighted by 25% (the rankings are assigned to all 141 CEFs, and not only to the top 30). The ranking for volume is not shown above because I was not sure about the time period used by CEFAnalyzer to calculate volume, as alluded to earlier. However, because I did not have time to manually calculate the ADV for 141 CEFs, the CEFAnalyzer data was still used to obtain a volume ranking for the funds, which was weighted by 25%. The weighted rankings were then summed, and the top 30 CEFs with the highest overall ranking are shown below, along with their composite individual ranks. A quick check on Yahoo Finance indicated that the 3-month ADV of these 30 CEFs was above the $1M cut-off (which is actually for the 6-month ADV, but I did not calculate this). Rank Ticker Yield Discount Volume Overall 1 (NYSE: RVT ) 8 8 18 10.50 2 (NYSE: BCX ) 14 1 25 13.50 3 (NYSEMKT: GGN ) 1 42 16 15.00 4 (NYSEMKT: GLO ) 11 7 39 17.00 5 (NYSE: NFJ ) 29 9 15 20.50 6 (NYSE: IGD ) 7 26 48 22.00 7 (NYSE: EXG ) 13 50 13 22.25 8 (NYSE: PCI ) 21 39 11 23.00 9 (NYSE: HIX ) 5 79 12 25.25 10 (NYSEMKT: EVV ) 30 35 17 28.00 11 (NYSE: DPG ) 33 10 38 28.50 12 (NYSE: AOD ) 44 2 24 28.50 13 (NYSE: NHF ) 4 17 96 30.25 14 (NYSE: DSL ) 18 77 8 30.25 15 (NYSE: CEM ) 9 100 5 30.75 16 (NASDAQ: CSQ ) 27 52 19 31.25 17 (NYSE: KYN ) 3 119 2 31.75 18 (NASDAQ: CHI ) 15 96 1 31.75 19 (NYSE: TDF ) 6 15 104 32.75 20 (NYSE: AWP ) 24 3 83 33.50 21 (NYSE: USA ) 31 20 58 35.00 22 (NYSE: BGB ) 36 46 26 36.00 23 (NYSE: NTG ) 25 88 6 36.00 24 (NYSE: FEI ) 22 97 7 37.00 25 (NYSE: BIT ) 47 22 32 37.00 26 (NYSE: UTF ) 54 11 29 37.00 27 (NYSE: BOE ) 40 41 30 37.75 28 (NYSE: GHY ) 39 47 27 38.00 29 (NYSE: ETJ ) 16 56 71 39.75 30 (NYSEMKT: FAX ) 41 5 72 39.75 At this point, I would like to compare notes with reader waldschm85 : I’ve attempted to follow the index methodology and came up with the below holdings from largest to smallest as of the open. How does this compare to your list Stanford Chemist?: BCX, TDF, GGN, RVT, KYN, PCI, NFJ, NTG, IGD, NHF, EXG, CSQ, GLO, DPG, CEM, , FEI, CHY, DSL, CHI, USA, HIX, PHK, GAB, TYG, EAD, ETJ, PTY, ETW, PFN, PCN Comparison of our two lists show that we have 20 out of 30 CEFs in common, which is quite high considering that [i] we did our analyses every days apart and [ii] I used an unspecified volume figure for ADV ranking while waldschm85 may have used a more accurate method. While the weighting methodology is too complex to be reproduced here, it can be noted that last year’s rebalance produced the CEF distribution shown below. The methodology states that no CEF can comprise more than 4.25% of the index. Additionally, the top 15 largest CEFs after last year’s rebalance all had weights of above 4%. I expect the weighting distribution of the 30 CEFs after this year’s rebalance to be quite similar to the last. Additions and deletions (predicted) Here we get to the interesting part! Which funds are completely new, and which will be completely removed? Which CEFs are in both 2015 and 2016 (predicted) portfolios? The following will be performed with my list of top 30 CEFs – obviously results will differ using waldschm85’s list or that of another person’s. CEFs are presented in alphabetical order. Added CEFs: BCX, BOE, CEM, CHI, CSQ, DPG, ETJ, FEI, IGD, KYN, NFJ, NHF, NTG, PCI, RVT, TDF, USA, UTF Removed CEFs: BGY, CHW, EAD, EDD, ERC, ESD, ETY, FPF, HYT, IGD, ISD, JPC, MRC, MMT, NCV, NCZ, PCI CEFs that remain from last year: AOD, AWP, BGB, BIT, DSL, EVV, EXG, FAX, GGN, GHY, GLO, HIX. The information above shows that 18 CEFs will be added to the index and 18 will be removed. 12 CEFs will remain in the index. This is a relatively high turnover but it is not unexpected given the fact that both the distributions and premium/discount values of CEFs can vary wildly. Moreover, given that I did not calculate weightings for the 2016 portfolio, I was unable to predict which CEFs will undergo the highest increases or decreases in allocation. However, it should be stressed that the above lists are only approximate. This is because I only performed a crude replication of the index methodology (specifically, I did not use the six-month ADV for either screening or ranking), and also because of the fact that the actual selection and ranking algorithm will be performed on CEF data at year-end rather than from today. Therefore, I am hesitant to recommend the buying of the CEFs to be added and the selling of CEFs to be removed as a potential strategy to profit from the upcoming rebalance. Use the information above at your own risk. Summary 2015 has not been a good year for CEFL unitholders. First, the botched rebalancing mechanism cause permanent loss of value in the index. Second, CEFL holders received 19.5% less income in 2015 compared to last year (this may be related to the first point). Third, CEFL shifted from a 60:40 equity:bond split in 2014 to a 33:67 equity:bond split this year, just in time for the oil-induced credit contagion to wreck havoc with the high-yield debt CEFs in the index. Certainly, a -32.7% YTD price return and -18.4% YTD total return cannot be described as anything other than disappointing for CEFL unitholders. CEFL data by YCharts Will 2016 bring brighter skies for CEFL? This I cannot say for certain. However, it is interesting to note that the predicted portfolio for 2016 contains several MLP CEFs, namely KYN, CEM, NTG, and FEI, whereas this year’s index contained none. Moreover, a myriad of high-yield bond funds will remain or are newly added to the predicted 2016 portfolio. Thus, it remains likely that the fate of CEFL will remain closely tied with the fortunes of the high-yield credit market for the foreseeable future.

Integrating Water Risk Analysis Into Portfolio Management

By Monika Freyman, CFA My previous article, ” Liquidity Risks of the H2O Variety ,” explored growing investor awareness about water risks within their portfolios and how that awareness plays into their investment decision making. Here, I will examine some of the increasingly sophisticated approaches that investors can take to integrate water risks into portfolio management. My recent survey of 35 institutional investors’ water integration practices found that while many investors think their methods, tools, and databases need to improve and evolve, they also found it worthwhile to integrate water into their research processes. And no wonder. As population pressures create competition for water, global groundwater supplies are declining and climate variability is increasing – leading to longer droughts and more intense flood events. All these factors pose risks that are hard to ignore. Water risk analysis happens at different stages of investment decision making, from the initial asset allocation strategies, to portfolio level analysis, through to the buy/sell decision. For example, one pension fund brought together portfolio managers from different asset classes to study how different markets, investment instruments, and geographic regions are exposed to the global water crisis. A few investors were also consistently analyzing their portfolio’s water risk exposure or its water footprint. Although far from a perfect approach – often missing location specific data or wastewater production metrics – portfolio water footprinting can be helpful in flagging companies and sectors with high water risk exposure relative to a benchmark and highlighting where further analysis is warranted. Various forms of portfolio analysis and attribution software allow managers to run water use metrics versus an index. For an example of water footprinting, see this South African study . At the individual security level, investors identified three critical research steps to obtain a comprehensive picture of water risk exposure: Understand Corporate Water Dependency: This varies by sector and, of course, company, with some industries relying heavily on access to abundant freshwater suppliers directly or in their supply chain. Corporate water dependency is not always easy to assess, but some companies are making the task easier by reporting their water use and wastewater trend data more consistently on their websites, in their annual reports, SEC filings or to data aggregating organizations, such as CDP Worldwide’s Water Program . Combine Water Dependency Data with an Assessment of Water Security: This gives a more comprehensive picture of corporate water risk exposure. A company may have high water needs but have their operations located in relatively water abundant regions. Another company, however, may be operating in regions of high water competition and drought. Such assessments are not simple to perform, but evolving tools, such as World Resources Institute (WRI)’s Aqueduct corporate water risks map , the World Wildlife Fund (WWF)’s Water Risk Filter , and other efforts are seeking to make the task easier. Get a Sense of Corporate Water Risk Awareness and Response: This step is essential because a company may have high water needs and poor water security, but mitigate the risks very effectively by elevating water issues to strategic decision making and putting water management and reporting systems in place. Tools such as The Ceres Aqua Gauge can be used to assess how well companies are managing their water and their exposure to water risks. For a more comprehensive list of third-party water tools and analytics, An Investor Handbook for Water Risk Integration is a helpful resource. Once water risk analysis is conducted on a corporation or security, our research found that fund managers use this information in a variety of ways, from avoiding high water risk industries or companies, to influencing internally created company environment, social, and governance (ESG) scores, to clarifying corporate engagement priorities. Several managers use their corporate water risk assessments to influence or modify financial projections or their weighted average cost of capital assumptions. For example, one fund manager studying companies in Brazil conducted scenario analysis modeling regarding how much the market cap of companies would be impacted if they had to absorb more of the costs of treating their wastewater discharges, especially as drought intensified and communities and regulators were becoming less tolerant of water use and pollution. Once impacts to market cap were assessed and shared with the management of those companies, engagement on those issues was far more pointed and productive. Other managers were trying to get a deeper understanding of the probability of large financial losses due to strategic risks related to water, such as not being able to grow revenue, access new markets, or develop new facilities. No matter what methodology one chooses to deepen water risk analysis practices, the most critical things to keep in mind are that water risks can lead to unlimited financial impact and loss. If a company loses access to water, a community kicks them out of a region due to water concerns, or permission to discharge wastewater is denied, the financial and strategic implications can be immense. For example, Newmont Mining (NYSE: NEM ) has postponed a $5 billion project in Peru due to community concerns over its water practices. In addition, it is important to look at sector specific issues, as water risks related to mining are obviously very different to those in semi-conductor manufacturing and so on. An Investor Handbook for Water Risk Integration includes a sector-specific cheat sheet on these issues. And most important of all: No matter how incomplete your water risk analysis starts off, it will likely provide a better understanding of sector or company risks (and opportunities) – which ultimately should add predictive power to your existing research processes. The goal is not to be perfect in your methods from the outset, but to begin including water risk analysis into your portfolio management practices. Disclaimer: Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.

Our Investing Biases Are Particularly Dangerous Because They Are Time-Based Rather Than Phenomenon-Based

By Rob Bennett I read an article this week that explored the differences between how we have responded as a society to the pushes for limits on smoking and on guns. The push for limits on smoking has been highly successful. The push for limits on guns has not been terribly successful. Why? The article argued that the difference is that smoking is not an ideological or cultural issue; neither conservatives nor liberals see efforts to limit smoking as an attack on their world view. It’s different with guns. Most cities are heavily liberal and most rural areas are heavily conservative. As a result, there are strong ideological and cultural differences between those who own guns and those who do not. Those who have never been around guns have a hard time understanding why anyone would feel a need to own one. But those who have been around guns all their lives cannot understand why those favoring limits on ownership are so troubled by guns. So efforts to change the law in this area produce intense conflicts; the harder one side pushes for limits, the harder the other side opposes those limits and gridlock results. “Bias” is not one thing. There are many varieties of biases, some more problematic than others. In fact, an argument can be made that some biases are good. As a general rule, it is a bad thing to be biased because to possess a bias is to respond unthinkingly to a phenomenon. But acting on the basis of a bias speeds up one’s reaction time and that is not such a bad thing in some cases. I have a strong bias against disco. I have probably missed out on some disco songs from which I would have derived a pleasurable listening experience. But there aren’t many disco songs that fall into that category. And my bias helped me avoid a lot of painful listening experiences too. The biases that many of us hold about investing issues are extremely damaging, in my view. Most biases are phenomenon-based. We favor certain types of food over others. Or we favor certain ways of thinking about issues over others. Or we favor certain ways of doing things over others. These biases can hold us back. But the good thing about phenomenon-based biases is that we can limit the power of the bias by deliberately exposing ourselves to the opposite sort of phenomenon from time to time to check whether the bias is supported by the realities. Liberals are biased against conservative ideas and conservatives are biased against liberal ideas. Is that really such a bad thing? If we reconsidered our philosophical orientation each time a new issue was presented to us for our assessment, it would take much longer for us to figure out where we stand on issues. The reality is that once a person has thought about a few issues hard enough to know where his bias lies, he can save time when assessing new issues by jumping to a quick conclusion that his position will be ideologically consistent with his earlier positions. Being biased is a time-saver. But there are dangers, of course. There are always those few issues regarding which a liberal adopts the conservative take and those few issues regarding which a conservative adopts the liberal take. Those exceptions can achieve great significance over time. If you follow the story of how a liberal becomes a conservative over a number of years or of how a conservative becomes a liberal over a number of years, you will see that it is usually one important exception to a general bias that starts the ball rolling in a new direction. I often seek out views different than my own just to shake up my preconceptions a bit. It’s very very hard to do that in the investing realm. The most important investing biases are time-based rather than phenomenon-based. That means that for long periods of time certain ideas are forgotten by almost the entire population. To tap into the other side of the story, the investor would have to study historical data from a time period many years removed from the current time period. Who does that? Shiller showed that valuations affect long-term returns. What he really was doing when he did that was showing that the stock market is not efficient, that mis-pricing on either the high or low side is a significant reality rather than the illusion that Buy-and-Holders believe it to be. Even during the most out-of-control bull market, there are a small number of people questioning whether the insane prices achieved are real and lasting. But the percentage of the population holding that view can be very small indeed. The percentage of the population that is conservative rather than liberal doesn’t vary dramatically from time to time. The percentage of the population that believes that stocks are the perfect investment choice is dramatically higher when prices are high than it is when prices are low. For a good number of years following the great crash of 1929, investors didn’t expect to see any capital appreciation at all on their stocks. The conventional wisdom of the time was that stocks were worth buying only for their dividends; those that didn’t pay high dividends were not worth owning. In the late 1990s, dividends fell to tiny levels. The very thing that made stocks dangerous (their high price) changed the conventional wisdom on stock ownership to reflect a bias that stocks are always worth owning. Stocks for the Long Run was a popular book in the 1990s. It would not have sold many copies in the 1930s. The book reports on data, facts, objective stuff. The message of the data should not change from times like the 1930s to times like the 1990s. But the ways in which we arrange the data and interpret the data changes when we go from bull markets to bear markets. People will be looking at the same data that was employed in Stocks for the Long Run to sell stocks to make the case against stocks when we are on the other side of the next stock crash. Our stock biases hurt us. But they are hard to see through because just about everyone is on one side of the table for a long stretch of time and then just about everyone is on the other side of the table for the next long stretch of time. Bull markets turn us all into bulls and bear markets turn us all into bears. Investing biases come to be so widely shared for long stretches of time that it is hard for any of us to keep their other point of view even remotely in mind. Disclosure: None