Tag Archives: literature

Another EXG-ETW Pairs Opportunity Presents Itself

Summary Mean reversion in CEFs can be exploited for small gains in portfolio performance. A previous article successfully capitalized on a premium/discount discrepancy between EXG and ETW. The current article identifies another potential opportunity to buy EXG (and sell ETW). Around one year ago, I wrote an article entitled ” Should You Sell ETW And Buy EXG? ” that described a pairs trading opportunity for these two funds. The Tax-Managed Global Buy-Write Opportunities Fund (NYSE: ETW ) and the Tax-Managed Global Diversified Equity Income Fund (NYSE: EXG ) are both global option income close-ended funds (CEFs) from Eaton Vance (NYSE: EV ). The main difference between the two CEFs is that ETW has around 100% option coverage while EXG has around 50% option coverage, with ETW therefore being the more defensive of the two funds. Both funds seek to achieve “current income with capital appreciation through investment in global common stock and through utilizing a covered call and options strategy.” See my previous article for further comparison regarding those two funds. The thesis of the pairs trade was based on the fact that ETW’s discount had narrowed to -3.31% (1-year premium/discount: -7.71%), while EXG’s discount remained high at -8.45% (1-year premium/discount: -8.33%). As was seen in a follow-up article ” Closing The EXG-ETW Pairs Trade “, the discount for ETW had widened from -3.31% to -3.93% while the discount for EXG had narrowed from -8.45% to -5.60%, leading to a gain of ~3% in 6 weeks (~23% annualized). While ~3% over six weeks doesn’t seem much, keep in mind that i) this works out to be ~23% annualized , and ii) this was a “dollar-neutral” trade , in that I merely sold my existing holdings of ETW and used the proceeds to buy EXG, while keeping the total dollar value of the investment constant. Had I held onto the trade for a bit longer, the EXG:ETW pair could have returned even more, up to ~12%. (click to enlarge) The mean reversion of CEF premium/discounts is something that has been documented in the literature (e.g. Patro et al. ). At the same time, a pairs trading strategy reduces risk by making dollar-neutral trades. Indeed, the similarity of EXG and ETW has made the EXG:ETW ratio trade within a tight range of ~10% for the past five years, as can be seen from the graph below. Highs in the graph represent good times to sell EXG and buy ETW, while lows in the graph represent good times to buy EXG and sell ETW. (click to enlarge) Current opportunity The chart above shows that the EXG:ETW ratio has again sank to the lower bound of the trading range. Why has this happened? As can be seen from the chart below, despite tracking each other closely for around ten months since October of last year, there has been a sudden dislocation of the price of the two funds over the past two months. EXG data by YCharts Most of this price disconnect is due to differential premium/discount behavior of the two funds. Over the past 3 months, EXG’s NAV total return was -4.74%, while its price total return was -10.44% (source: CEFConnect ). On the other hand, ETW’s NAV total return was -4.12%, while its price total return was “only” -5.42%. Another way of stating this data is that EXG’s discount has expanded more than ETW’s. EXG has a current discount of -11.08% (1-year average: -6.24%) while ETW has a current discount of -6.70% (1-year average: -5.03%). This means that EXG is more attractive from a valuation standpoint compared to ETW. Note that world stocks (via the iShares MSCI ACWI (All Country World Index) Index ETF ( ACWI)) suffered a 3-month total return of -8.55%, meaning that both EXG and ETW outperformed their benchmark, as would be expected for option-income funds during stock market downturns. The 1-year premium/discount history of EXG is shown below (CEFConnect). We can see that its current discount is at its widest point for the past one year. (click to enlarge) The 1-year premium/discount history of ETW is shown below (CEFConnect). Based on the above analysis, a pairs trading strategy would entail selling ETW and buying EXG. Given that both funds have very similar 5-year average discount values (-9.45% for EXG and -8.90% for ETW), a reversion of EXG’s current discount of -11.08% and ETW’s current discount of -6.70% would allow investors to profit from the trade. Risks In my previous article, I wrote: More defensive funds (the ones with higher option coverages) are getting more expensive relative to the less defensive funds…What could one take away from this? One might infer that market participants are worried about an impending market correction, and are bidding up more defensive option income funds. It appears that the same phenomenon may be happening right now. As ETW has 100% option coverage, it is more defensive than EXG at 50% option coverage. Indeed, in 2011, ETW eked out a positive NAV total return performance of +0.98%, while EXG declined by -3.33%. By comparison, ACWI fell -7.60%. Thus, a risk of this pairs strategy is that if a market correction were to occur, ETW will likely fall less than EXG. Still, the high current discount of EXG does provide a margin of safety whatever happens. Top holdings The top holdings of EXG and ETW as of 7/31/2015 are shown below (source: CEFConnect). EXG Google Inc (NASDAQ: GOOG ) $109.01M 3.49% Ev Cash Reserves Fund 0.12 06 Aug 2015 $67.98M 2.18% Nike, Inc. B (NYSE: NKE ) $64.89M 2.08% Apple, Inc. (NASDAQ: AAPL ) $64.18M 2.06% Exxon Mobil Corporation (NYSE: XOM ) $58.57M 1.88% Home Depot, Inc. (NYSE: HD ) $56.87M 1.82% Roche Holding AG ( OTCQX:RHHBY ) $53.33M 1.71% Walt Disney Co (NYSE: DIS ) $52.62M 1.69% Prudential Financial (NYSE: PRU ) $51.89M 1.66% Medtronic, Inc. (NYSE: MDT ) $51.25M 1.64% Nippon Telegraph and Telephone Corp. (NYSE: NTT ) $50.25M 1.61% ETW Apple, Inc. $62.02M 4.61% Microsoft Corporation (NASDAQ: MSFT ) $36.47M 2.71% Amazon.com Inc (NASDAQ: AMZN ) $25.20M 1.87% Nestle SA ( OTCPK:NSRGY ) $24.41M 1.81% Novartis AG (NYSE: NVS ) $22.71M 1.69% Roche Holding AG $21.95M 1.63% Google Inc $20.61M 1.53% Gilead Sciences Inc (NASDAQ: GILD ) $20.32M 1.51% Fast Retailing Co., Ltd. ( OTCPK:FRCOY ) $19.59M 1.46% Google, Inc. Class A (NASDAQ: GOOGL ) $18.76M 1.39% Comcast Corp A (NASDAQ: CMCSA ) $17.91M 1.33% Summary I really like both EXG and ETW as option-income funds. Over both past 3-year and 5-year periods, both funds have achieved comparable total return performances with ACWI, but with lower volatility, resulting in higher Sharpe ratios compared to the benchmark ETF. Investors who own both EXG and ETW can consider further “juicing up” their portfolio returns by taking advantage of mean reversion in premium/discount values of the two CEFs. The current discount of -11.08% for EXG is more attractive than ETW’s at -6.70%, which suggests that investors could swap existing holdings of EXG for ETW. However, one risk of this strategy is that in a prolonged market correction, ETW will perform better than EXG, being the more defensive of the two funds.

ETF Update: John Hancock, Goldman Sachs, JPMorgan And More Launched Funds This Week

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 weekend, or every other weekend (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. Last week we saw the first Goldman Sachs (NYSE: GS ) ETF enter the arena, the ActiveBeta U.S. Large Cap Equity ETF (NYSEARCA: GSLC ). While there was a followup launch from GS this week, John Hancock made the biggest splash with its first 6 ETF offerings. The newcomer has a strong history in mutual funds and I am excited to see how these new ETFs perform in the coming months. Fund launches for the week of September 28, 2015 Another Goldman ETF opens for business (9/29): One week after the launch of GSLC, Goldman Sachs rolls out one for emerging markets , the Goldman Sachs ActiveBeta Emerging Markets ETF (NYSEARCA: GEM ). John Hancock adds 6 new funds (9/29): As stated by Andrew G. Arnott, president and CEO of John Hancock Investments, “it was important to us to develop an ETF product that seeks to address investor needs for performance potential, backed by an investment approach rooted in decades of academic research.” They are the John Hancock Multifactor Mid Cap ETF (NYSEARCA: JHMM ), the John Hancock Multifactor Large Cap ETF (NYSEARCA: JHML ), the John Hancock Multifactor Technology ETF (NYSEARCA: JHMT ), the John Hancock Multifactor Healthcare ETF (NYSEARCA: JHMH ) and John Hancock Multifactor Financials ETF (NYSEARCA: JHMF ). John Hancock doesn’t seem to have pages for the 6 funds yet, but the SEC filing linked above should be a good starting point for interested investors. JPMorgan (NYSE: JPM ) launches a new U.S. Equity ETF (9/30): The JPMorgan Diversified Return U.S. Equity ETF (NYSEARCA: JPUS ) tracks the Russell 1000 Diversified Factor Index , which “seeks to provide U.S. exposure with the potential for better risk-adjusted returns.” Credit Suisse rolls out an income ETF (9/30): The Credit Suisse X-Links Multi-Asset High Income ETN (NYSEARCA: MLTI ) tracks an index “comprised of a broad, diversified basket of up to 120 publicly-traded securities that historically have paid high dividends or distributions.” IndexIQ launches a new fund-of-funds ETF (9/30): The IQ Leaders GTAA Tracker ETF (NYSEARCA: QGTA ) follows the IQ Leaders GTAA Index, which “seeks to track the performance and risk characteristics of the 10 leading global allocation mutual funds. Identifying 10 leading mutual funds is based on fund performance and asset size and is reconstituted annually.” iShares launches a hedged alternative to Japanese equities (10/1): The iShares Currency Hedged JPX-Nikkei 400 ETF (NYSEMKT: HJPX ) “seeks to track the investment results of a broad-based benchmark composed of Japanese equities.” It is a hedged alternative for the iShares JPX-Nikkei 400 ETF (JPXN). There were no fund closures for the week of September 28, 2015 One of the first comments on my article last week raised an important question : The ETF world is ever changing. Smart beta was a new thing recently. Similarly I saw few articles that talked about ETMF (Exchange Traded Mutual Funds) being the next big thing. Would love to see some research on it.. Our own Jonathon Liss came up with an answer that I feel many readers will find incredibly helpful as ETMFs start to gain traction in the market: ETMFs are not really ETFs. In fact, I think the term is intentionally confusing in an attempt to ride the popularity of ETFs. In most key ways, these products are no different than mutual funds. The fact they are ‘exchange-listed’ is meaningless for all intents and purposes. They only price once a day and are non-transparent meaning they only have to list their holdings once per quarter akin to MFs – and on a 1-2 month delay at that as is standard with 13F filings. Additionally, they have a strange auction bidding system required to buy them. They do likely share some of the theoretical tax advantages of ETFs but that’s about it. Thus, I think they should essentially be lumped with mutual funds and not ETFs. If I’m missing key details I’d be happy for others to fill me in but this is what I’ve been able to gather from the literature I’ve seen. 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. Share this article with a colleague

Combining Volatility, Momentum, And Trend In Asset Allocation

Summary Risk-based portfolio strategies are popular in the asset management industry. Three common strategies are Minimum Variance (MV), Equal Risk Contribution (ERC) and Maximum Diversification (MD). These strategies do not depend on asset returns’ forecasts and they are based on a single criterion: risk. With higher returns and lower risk, risk-based portfolios that use moving averages have higher Sharpe ratios than initial risk-based portfolios. High momentum risk-based portfolios, by contrast, have higher risk, which is largely compensated for by higher returns. By Gregory Guilmin The Effective Combination of Risk-Based Strategies with Momentum and Trend Following Abstract: The Efficient Market Hypothesis (EMH) has been widely called into question in the investment literature, through two main anomalies: timing and low-volatility anomalies. In this paper, we aim to combine the predictive power of timing and low-volatility strategies to deliver better risk-adjusted portfolio performance. We adopt a two-step approach for a constant dataset composed of 18 country MSCI stock market indices over the 1975-2014 period. First, we use different timing strategies: moving averages and momentum. We select stock market indices based on different moving averages (6, 8, 10, and 12 months), while the momentum strategy ranks the different stock market indices into momentum subsets (low, medium, and high momentum). After the first step using the different timing strategies, the second step consists in building risk-based portfolios (MV, ERC, and MD) as well as 1/N benchmark portfolios for each of these timing strategies. Our results highlight the effectiveness, the relevance and the robustness of our approach. First, risk-based portfolios using relevant timing strategy indeed provide better returns, lower volatilities, higher Sharpe ratios, and lower Value-at-Risk (VaR) and Expected Shortfall (ES) than traditional risk-based portfolios. The second contribution of our approach features that risk-based strategies provide better risk-adjusted returns and lower VaR and ES than the 1/N portfolio within a context in which the first step is dedicated to the application of a relevant timing strategy. Finally, among these risk-based portfolios using relevant timing strategies, the MD and MV portfolios usually obtain the best risk-adjusted performance. Alpha Highlight: Risk-based portfolio strategies are popular in the asset management industry. Three common strategies are Minimum Variance (MV), Equal Risk Contribution (ERC) and Maximum Diversification (MD). These strategies do not depend on asset returns’ forecasts and they are based on a single criterion: risk. The interest in estimation procedures relying on a risk measure could be explained by three major factors: Reconsideration of the importance and the relevance of portfolio risk management. Better predictability of security variance and covariance risks by comparison with expected returns. The outperformance of the “low-volatility anomaly.” Details here . In addition to the low-volatility anomaly, a large number of authors have talked about the “momentum anomaly.” The momentum effect has been emphasized by Jegadeesh and Titman (1993). Momentum strategies are profitable in most major stock markets worldwide and this outperformance of momentum strategies is consistent over time. Linked to this concept of cross sectional momentum, time-series momentum, such as trend following strategies, have been identified. Several academic papers show that moving average trading rules have predictive power for future returns, and that trend following strategies with moving averages are effective in practice (Among others, see Brock et al., 1992; Clare et al., 2014; Faber, 2007, 2013; Hurst et al., 2010 and ap Gwilym et al., 2010). Methodology: Given this backdrop, in which risk-based strategies and timing strategies have been developed in the literature, the purpose of this paper is to combine the two strategies. This two-step approach consists in applying a timing strategy (either a moving average or a momentum strategy in the first step) followed by risk-based portfolio optimization procedures (second step). We compute risk-based and equally weighted (as a benchmark) portfolios with and without timing strategies in the first step for a constant empirical dataset composed of 18 country MSCI stock market indices. The estimation period ranges from January 1975 to December 2014. To the best of our knowledge, this paper is the first to shed light on the combination of timing and risk-based strategies. First Step: Selection of the stock market indices The methodology consists of two steps. In the first step, moving averages are used to select stock market indices that perform well (by exhibiting an upward trend) and that are used in the second step of our analysis (i.e., in the risk-based and 1/N portfolio optimization). Stock market indices exhibiting a negative trend are not selected as an input in the portfolio optimization procedure. If the price of the stock market index is above its x − month moving average, then this index is selected for the portfolio optimization procedure. Conversely, if it crosses below its x − month moving average, then the stock market index is not selected for the second step. We use moving averages of varying lengths: 6, 8, 10, and 12 months. To add an additional timing strategy, we also select stock market indices in accordance with the concept of momentum, in which a stock market’s performance relative to its peers predicts its future relative performance. As long-term investors, the momentum strategy involves ranking stock market indices based on their past 12-month performance and splitting them into three subsets, depending on the value of their momentum compared with one of their peers. The three subsets are the low, medium and high momentum subsets, respectively. Second Step: Portfolio optimization After selecting stock market indices following the different timing strategies of the first step, the second stage consists in applying different portfolio optimization procedures to find the optimal weights of the selected stock market indices. Selection (1st step) and weighting (2nd step) are adjusted simultaneously, i.e., on a monthly basis (end of month). We apply three risk-based portfolio strategies (Minimum Variance, Equal Risk Contribution and Maximum Diversification) as well as the 1/N benchmark portfolio strategy, usually considered a relevant benchmark in the literature. First, the Minimum Variance portfolio aspires to minimize the global variance of the portfolio. Second, the Equal Risk Contribution portfolio is the portfolio in which the risk contribution is the same for all assets in the portfolio. Finally, the Maximum Diversification portfolio (also called the Most Diversified Portfolio), introduced by Choueifaty (2006), is the portfolio that maximizes diversification. Diversification is computed using the diversification ratio. The diversification ratio is defined as the ratio of its weighted average volatility to its portfolio volatility. Main Findings: The table below summarizes results based on a constant dataset composed of 18 country MSCI stock market indices between 1975 and 2014. We can see that all portfolios that employ moving averages in the first step perform better than initial risk-based portfolios . Regarding momentum, high momentum risk-based strategies offer better annual performance than initial risk-based portfolios. Table 1: Portfolio performances with the constant country MSCI Indices sample (1975-2014) Annualized Annual Annualized Sharpe VaR (1%) ES (1%) returns (%) returns (%) Volatility (%) Ratio (%) (%) Initial Portfolios 1/N 8.931 10.013 16.723 0.599 -13.118 -18.531 MV 8.152 8.866 14.017 0.632 -10.987 -14.785 ERC 8.785 9.781 16.131 0.606 -12.550 -18.116 MD 8.624 9.655 16.275 0.593 -14.254 -17.890 With timing strategies 6 months 1/N 10.032 10.732 14.965 0.717 -11.946 -15.482 MV 9.987 10.472 13.439 0.779 -10.238 -13.884 ERC 9.605 10.256 14.387 0.713 -11.592 -15.107 MD 10.515 11.166 14.882 0.750 -11.966 -15.843 8 months 1/N 10.401 11.062 14.897 0.743 -11.946 -15.363 MV 10.626 11.056 13.419 0.824 -10.212 -13.961 ERC 10.107 10.694 14.218 0.752 -11.592 -15.000 MD 11.079 11.650 14.664 0.794 -11.966 -15.787 10 months 1/N 9.832 10.534 14.842 0.710 -11.665 -15.258 MV 9.299 9.853 13.499 0.730 -10.871 -14.453 ERC 9.486 10.119 14.170 0.714 -11.311 -15.026 MD 10.191 10.855 14.744 0.736 -11.966 -16.022 12 months 1/N 10.612 11.229 14.725 0.763 -11.904 -15.220 MV 10.523 10.957 13.358 0.820 -10.602 -14.488 ERC 10.494 11.020 14.021 0.786 -11.621 -15.066 MD 11.206 11.766 14.671 0.802 -12.325 -16.304 Low momentum 1/N 5.359 7.021 18.821 0.373 -14.322 -19.843 MV 5.533 6.855 17.063 0.402 -13.632 -17.501 ERC 5.162 6.704 18.127 0.370 -14.152 -19.464 MD 5.773 7.233 17.884 0.404 -13.852 -19.023 Medium momentum 1/N 8.919 9.997 16.747 0.597 -12.766 -17.035 MV 9.245 10.101 15.602 0.647 -11.012 -15.796 ERC 9.069 10.087 16.456 0.613 -12.456 -16.953 MD 9.247 10.309 16.770 0.615 -14.402 -18.084 High momentum 1/N 11.896 13.022 18.266 0.713 -14.637 -20.915 MV 12.583 13.446 17.231 0.780 -11.539 -18.729 ERC 12.16 13.178 17.834 0.739 -13.770 -20.125 MD 12.092 13.218 18.356 0.720 -13.443 -20.719 Market-cap weighted benchmarks MSCI World 8.016 8.839 14.782 0.598 -11.073 -14.564 MSCI World Momentum 11.432 12.130 15.817 0.767 -11.517 -14.965 The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Trend following and high momentum strategies are effective for 1/N portfolio optimization but also for risk-based portfolios because they produce better annual returns compared with initial risk-based portfolios. With respect to risk measures such as volatility, Value-at-Risk (VaR) and Expected Shortfall (ES), risk-based portfolios that employ moving averages exhibit lower volatility than initial risk-based portfolios as well as lower VaR and ES. This finding is important because it enables investors to reduce the risk to which they are exposed. With higher returns and lower risk, risk-based portfolios that use moving averages have higher Sharpe ratios than initial risk-based portfolios. High momentum risk-based portfolios, by contrast, have higher risk, which is largely compensated for by higher returns. Therefore, such portfolios are characterized by higher Sharpe ratios than initial risk-based portfolios. This paper documents the effectiveness, in terms of risk and return, of the use of these relevant timing strategies combined with risk-based portfolio strategies. In addition to that, robustness checks were also conducted with different other datasets, different estimation periods as well as different parameters of the variance-covariance matrix. Original Post