Tag Archives: seeking-alpha

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 .

Lipper U.S. Fund Flows: Gains For All 4 Fund Groups

By Patrick Keon Lipper’s fund macro-groups (including both mutual funds and exchange-traded funds [ETFs]) had aggregate net inflows of $14.0 billion for the fund-flows week ended Wednesday, October 14. This activity marked the second consecutive week of overall positive flows; the groups took in $11.8 billion of net new money the prior week. The wealth was spread out this past week, with all four fund macro-groups experiencing positive net flows: money market funds (+$7.9 billion) led the pack, followed by taxable bond funds (+$3.1 billion) and equity funds (+$2.5 billion), while municipal bond funds contributed $521 million. The downturn at the end of the week was triggered by weak economic data from both domestic and foreign sources. Reports out of China again raised global growth concerns. China’s economic growth for Q3 2015 was forecasted to be 6.8%, the lowest level since 2009, giving investors concerns as to whether the slump in the world’s second largest economy is worse than originally thought. On the home front, corporate earnings and a gloomy picture of U.S. growth weighed on the markets. Wal-Mart (NYSE: WMT ) issued a much weaker-than-expected profit forecast, which-coupled with the release of a weak U.S. retail sales report-resulted in a sell-off in the retail sector. The Federal Reserve’s Beige Book pointed toward a continued slowdown in U.S. growth. With economic data continuing to point to weakness and the inflation rate sitting well below the target rate of 2.0%, it seems the likelihood of the Fed raising interest rates in 2015 is getting slim. The week’s positive flows into money market funds (+$7.9 billion) marks the fourth consecutive week of net inflows for the group over which time they have taken in almost $42 billion. Institutional money market funds were responsible for the lion’s share of the positive flows last week, taking in $8.2 billion in net new money this past week. ETFs (+4.7 billion) were responsible for all of the equity net inflows for the week, while equity mutual funds saw $2.2 billion leave their coffers. The Powershares QQQ Trust ETF ( QQQ , +$1.3 billion ) and the iShares Russell 2000 ETF ( IWM , +$911 million ) had the two largest net inflows on the ETF side, while for mutual funds both domestic (-$1.6 billion) and nondomestic (-$700 million) equity funds experienced net outflows. ETFs (+$2.6 billion) contributed the majority of the net new money for taxable bond funds, while taxable bond mutual funds chipped in almost $500 million. The iShares iBoxx $ High Yield Corporate Bond ETF ( HYG , +$616 million ) and the iShares iBoxx $ Investment Grade Corporate Bond ETF ( LQD , +$608 million ) were the two largest contributors to the positive flows for ETFs. Lipper’s High Yield Funds (+$378 million) and U.S. Mortgage Funds (+$326 million) classifications had the two largest increases for mutual funds. Municipal bond mutual funds took in $482 million of new money for their second straight week of net inflows. The majority of these inflows (+$319 million) came from funds in Lipper’s national municipal bond fund groups.

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.