Tag Archives: etfs

Closed End Funds: Is There An Opportunity?

Summary Closed End Funds have traded for years, yet tend to be misunderstood. There are both advantages and disadvantages to investing in CEFs. At the present time, there are a number of compelling CEF trading at deep discounts. Closed End Funds (CEFs) have been around for decades, but despite their lengthy existence they tend to be misunderstood and consequently are under-appreciated investment vehicles. In contrast to open end mutual funds, which have the freedom to issue unlimited shares at the fund’s Net Asset Value or NAV, CEFs issue a fixed number of shares. In order to provide liquidity to current and future investors, CEFs list their stock on an exchange (e.g. NYSE). CEF shares transact at a market price, which very often differs from its NAV price. The price of a CEF may be above (premium) or below (discount) its NAV. The purpose of this paper is to discuss the merits and issues associated with CEF investments and to focus the reader’s attention on the current opportunity in the space. There are a few advantages to investing in CEFs, the largest being the opportunity to buy a fund at a discount to its NAV; as the discount narrows over time, the added return can be substantial. Another advantage of CEFs is that management has dedicated capital with which to invest; there is never a concern that cash will be needed to meet unexpected redemptions in times of stress. It is well documented that redemptions from panic stricken investors at market lows have hurt open end fund returns. In contrast, investing in closed end funds requires careful monitoring of discounts as they vary constantly. Another less appealing attribute is the higher expense ratios CEF tend to charge, while in addition an investor’s trading costs should also be evaluated. Trading costs can be significant if the float or average daily volume is low. Lastly, since most CEFs employ leverage, the amount and costs associated with borrowing needs to be carefully considered. At Lynx, we have been opportunistically investing in CEFs for several years. We think it is prudent in some cases to substitute closed end funds for open ended funds and vice-versa based on the attractiveness of the discounts. During the volatile months of August and September the average discount on taxable fixed income CEFs was approximately 11.5%, compared to an average discount of 4.5% over the last 20 years. The chart below provides data from the Closed End Fund Association. Based on the data, CEFs in various categories are trading at their deepest discounts. A few examples of opportunities today follow, but we caution readers to discuss the associated risks with their financial advisors prior to investment. The first example is a CEF of preferred stocks, the John Hancock Premium Dividend Fund (NYSE: PDT ). Unlike most preferred stock funds, the John Hancock team’s specialty is utility companies. As of October 11, 2015, the fund had a distribution yield 8.2%, was 33.5% levered and traded at an 11.3% discount (PDT Premium/Discount chart). Another example is the Blackrock Corp High Yield Fund (NYSE: HYT ). This fund is actively managed by the Blackrock team and invests in high yield bonds and bank loans. As of October 11, 2015, HYT was trading at a 13.7% discount (HYT Premium/Discount chart) and had a distribution yield of 8.2%, with 30% leverage. (click to enlarge) *Data: Lipper, A Thomson Reuters Company; Chart: Lynx Investment Advisory PDT Discount/Premium Over 5 Years (click to enlarge) HYT Premium/Discount Over 5 Years (click to enlarge) * Charts: CEFConnect.com In summary, CEFs have their merits and limitations. At times, CEFs can be bought for deep discounts that ultimately can boost investor returns. In our opinion, the current environment is offering many closed end funds at record discounts. Therefore, in our opinion, many CEFs offer a compelling opportunity in the current market environment.

Goldman Sachs Serves Up Plain Talk On Smart Beta

By DailyAlts Staff What do most potential investors think about smart beta? In Goldman Sachs’ (NYSE: GS ) experience, they don’t – only a handful of investors have any idea what “smart beta” is, and most are confused by the distinction between “active” and “passive” investing. For this reason, Goldman Sachs thinks advisors need to serve up “plain talk” in explaining smart beta to their clients, and the firm shares ideas of how to accomplish this in the October 2015 edition of its Strategic Advisory Solutions white paper series. What Smart Beta Isn’t Goldman Sachs defines “smart beta” as referring to “rules-based investment strategies which seek to outperform a traditional market index or reduce risk versus that index,” but the firm admits that this definition is overly “technical” – and therein lies the challenge. Advisors are tempted to define smart beta by what it isn’t – i.e., cap-weighted. But in Goldman’s focus groups, a surprisingly low number of investors understood what “cap-weighted” even meant. Most were happy with their index ETFs, and when asked how ETFs could be improved, Goldman was generally met with silence. Thus, the “market-weight critique” – wherein advisors explain that cap-weighted indexes inevitably overweight overpriced stocks – is a “flawed” approach, in Goldman’s view. Plainer Talk Another popular way to describe smart beta to novices is to say it “blends” active and passive elements. Unfortunately, many of Goldman’s focus-group participants thought “active management” referred to frequent trading, and “passive management” meant “letting an advisor do the work for you.” Investors may be in desperate need of basic investment education, but in the meantime, advisors can address them with plainer talk – especially when discussing smart beta. Instead of defining it by what it’s not , or by talking about active versus passive management, Goldman recommends advisors explain the similarities between smart beta and traditional cap-weighted investing, while acknowledging the differences that can help smart beta outperform the broad market. Goldman’s Active Beta ETFs Goldman Sachs launched a pair of new active-beta ETFs itself last month. The first, the Goldman Sachs ActiveBeta U.S. Large Cap Equity ETF (NYSEARCA: GSLC ), debuted on September 17; while the second, the Goldman Sachs ActiveBeta Emerging Markets Equity ETF (NYSEARCA: GEM ), launched eight days later. The former quickly attracted more than $78 million assets under management (“AUM”), while the latter’s AUM tops $181 million. Both are based on ActiveBeta indexes that are designed to beat cap-weighted equivalents by weighing stocks according to four criteria: Value, Momentum, Quality, and Low volatility GSLC applies this methodology to U.S. large-cap equities. GEM does the same for stocks from emerging-market countries. Future Goldman ActiveBeta ETFs will apply the indexing strategy to European, international, Japanese, and U.S. small cap stocks. Smart Beta as Blank Slate The good news about widespread ignorance of smart beta is that advisors can approach clients with a blank slate. Goldman thinks advisors should explain that smart beta is like traditional index-fund investing, in that investments are selected by rules-based methodologies, but that smart-beta indexes are designed to outperform cap-weighted indexes by tilting towards favorable “factors” such as value or low volatility. Advisors shouldn’t try to get their clients to think about smart beta as something “radically different,” in Goldman’s view. Instead, smart beta should be considered a way to potentially outperform the broad market, while not paying a lot in fees. That’s the kind of “plain talk” everyday investors can appreciate. For more information, download a pdf copy of the white paper .

Simple ETF Portfolio Performance With Monthly Reallocation By Mean-Variance-Optimization

Summary The simple ETF portfolio with monthly reallocation performed better than the equal weight portfolio in 2015. The low and mid risk portfolios had good positive returns, while the high risk portfolio had a very small loss. Even the high risk portfolio performed better than the equal weight portfolio. The simple ETF portfolio was introduced in an article published in August 2015. Since then the markets suffered a mini crash and a correction associated with high volatility and very negative market sentiment. Investors all over the world moved large amount of money out of the stock market and into other “perceived safer” asset classes such as bonds. It is appropriate, therefore, to ask ourselves how an adaptive strategy is dealing with this kind of market environment. In this article we analyze the performance of the simple ETF portfolio, emphasizing its results during the latest period of high market turbulence. For completeness, we will review the historical performance of the portfolio since January 2003, but will discuss in more detail its performance during the first nine months of 2015. The portfolio is made up of the following four ETFs: SPDR S&P MidCap 400 ETF (NYSEARCA: MDY ) PowerShares QQQ Trust ETF (NASDAQ: QQQ ) iShares 1-3 Year Treasury Bond ETF (NYSEARCA: SHY ) iShares 20+ Year Treasury Bond ETF (NYSEARCA: TLT ) Basic information about the funds was extracted from Yahoo Finance and marketwatch.com and it is shown in table 1. Table 1. Symbol Inception Date Net Assets Yield% Category MDY 5/04/1995 14.23B 1.41% Mid-Cap Blend QQQ 3/03/1999 36.93B 0.96% Large Growth SHY 7/22/2002 13.11B 0.48% Short Term Treasury Bond TLT 7/22/2002 6.41B 2.62% Long Term Treasury Bond The data for the study were downloaded from Yahoo Finance on the Historical Prices menu for MDY, QQQ, SHY and TLT. We use the daily price data adjusted for dividend payments. For the adaptive allocation strategy, the portfolio is managed as dictated by the mean-variance optimization algorithm developed on the Modern Portfolio Theory ( Markowitz ). The allocation is rebalanced monthly at market closing of the first trading day of the month. The optimization algorithm seeks to maximize the return under a constraint on the portfolio risk determined as the standard deviation of daily returns. The portfolios are optimized for three levels of risk: LOW, MID and HIGH. The corresponding annual volatility targets are 5%, 10% and 15% respectively. In Table 2 we show the performance of the strategy applied monthly from January 2003 to September 2015. Table 2. Performance of MVO algorithm applied monthly versus an equal weight portfolio.   TotRet% CAGR% VOL% maxDD% Sharpe Sortino 2015 return LOW risk 167.65 8.03 5.60 -5.59 1.43 1.99 3.16% MID risk 399.09 13.45 10.61 -10.34 1.27 1.68 3.60% HIGH risk 697.85 17.70 16.40 -17.18 1.08 1.52 -0.33% Equal weight 204.71 9.14 9.58 -24.50 0.95 1.29 -1.33% Please notice that the realized volatilities are well correlated with the target values. In fact, the realized volatilities are just slightly greater that the target values. Also, as expected, the realized annual returns are also well correlated to the volatility targets. All the values in the CAGR% column are a little greater than the realized volatilities in the VOL% column. The 2015 returns column shows that all MVO strategies performed better than the equal weight portfolio. The LOW and MID risk portfolios achieved a positive return of over 3% while the equal weight portfolio lost 1.33%. The HIGH risk portfolio lost a minute 0.33%. The equity curves for all portfolios are shown in Figure 1. (click to enlarge) Figure 1. Equity curves of the portfolios with MVO monthly optimization and equal weight allocation. Source: All charts in this article are based on calculations using the adjusted daily closing share prices of securities. We see in figure 1 that the equity of the LOW risk portfolio had a constant, very stable, rate of increase over the entire time of the simulation. It was almost unaffected by any market event. By contrast, the equity of the equal weight strategy with rebalancing shows the highest variability and the highest loss during the 2008-09 crises. The equal weight strategy worked quite well during long bullish periods of the market such as during 2003-07 and 2009-14. The MID and HIGH risk strategies worked extremely well during the 2009-14 period with a very brief periods of mild correction in 2011. All strategies show a flattening of their equity curves during 2015. In Figures 2, 3 and 4 we show the time allocation for all MVO strategies from January 2014 to September 2015. We decided to display the allocations over a shorter most recent time interval in order to get graphs that are easy to read. (click to enlarge) Figure 2. In figure 2 we see that the LOW risk strategy allocated, on average, over 60% of the money to the bond funds. About 30% to 40% was allocated alternately to QQQ or MDY. (click to enlarge) Figure 3. In figure 3 we see that in 2014 the money was allocated alternately between TLT and QQQ. The first half of 2015 the allocation went to MDY and TLT. In July and August of 2015 the money was allocated to QQQ and SHY, switching all to TLT and SHY in September and October. (click to enlarge) Figure 4. In figure 4 we see that the HIGH risk strategy allocates the money to a single asset at any time. Since January 2014 it simply alternated between QQQ and TLT. This strategy worked very well most of the time, but in the first nine months of 2015 it suffered a very small loss. In table 4 we show the current allocations for all the strategies. Table 3. Current allocations for October 2015.   MDY QQQ SHY TLT LOW risk 0% 0% 69% 31% MID risk 0% 0% 35% 65% HIGH risk 0% 0% 0% 100% As seen in table 3 all portfolios are invested only in bond funds, regardless of risk level. The low risk portfolio in mostly invested in the short term, while the high risk is 100% in long term treasuries. Conclusion The simple ETF portfolio with monthly reallocation performed better than the equal weight portfolio in 2015.The low and mid risk portfolios had good positive returns, while the high risk portfolio had a very small loss. Additional disclosure: The article was written for educational purposes and should not be considered as specific investment advice.