Tag Archives: construction

5 Sector Favorites For Q3 Earnings And Their Hot ETFs

The Q3 earnings season has just kicked in and investors are worried about the impact that the China-led global growth concerns will have on the earnings picture. Adding to the woes were some Q2 issues like sluggishness in other developed and developing economies, lower oil prices, a strong dollar, uncertain timing of the rates hike, and a slump in commodities that spilled over into Q3. All these factors would continue to heighten the financial market instability and could dampen earnings growth. This is especially true as Q3 earnings estimates have fallen substantially over the past three months from a decline of 2.7% to decline of 5.6% as per the Zacks Earnings Trend . This is worse than earnings decline of 2.2% reported in Q2. Revenues are also expected to decline by 5.5% versus the 6.5% decline in Q1. While the earnings weakness seems broad based with energy being the biggest drag, autos and transportation are the only sectors with double-digit growth. Further, the earnings growth rates for medical, construction and financial sectors are strong (read: 2 ETFs Rising to Rank #1 This Earnings Season ). Given this, we have highlighted five ETFs – each from these expected winning sectors – that investors should definitely tap this earnings season. Not only are these picks far better in today’s investment world, they are also likely to outperform the overall market in the coming weeks. Automotive The U.S. automotive sector has been riding high with the overall industry on track to record its best year of sales since 2000. Increased consumer spending, lower gasoline prices, rising income, high demand for light trucks, a plethora of new models, need to replace aging vehicles and the easy availability of credit at lower interest rates are adding adequate fuel to the industry. These attributes will lead to a strong auto earnings growth of 21.2%, making it the best sector of the third quarter despite the big Volkswagen scandal. Investors could ride the earnings growth potential with a pure play – First Trust NASDAQ Global Auto Index ETF (NASDAQ: CARZ ) – that provides global exposure to the 37 auto stocks by tracking the NASDAQ OMX Global Auto Index. Japanese firms dominate the fund’s portfolio with more than one-third share and the top five holdings account for at least 8% share each. CARZ is under appreciated as indicated by its AUM of only $32.5 million and average daily trading volume of under 8,000 shares. The product charges 70 bps in fees per year and has a Zacks ETF Rank of 2 or ‘Buy’ rating with a High risk outlook. Transportation The transport sector is expected to report earnings growth of 17.0% year over year for the third quarter. While a strong dollar is eating away the profits of big transporters, the sector remains the biggest beneficiary of cheaper oil prices, and increasing consumer confidence and spending. Further, higher demand for the movement of goods across many economic sectors acts as a major catalyst for earnings growth. One way to play this trend is with the iShares Transportation Average ETF (NYSEARCA: IYT ) , which tracks the Dow Jones Transportation Average Index and holds 20 stocks in its basket. The fund is highly concentrated on the top firm – FedEx (NYSE: FDX ) – at 11.8% while other firms hold less than 8.1% of assets. Air freight & logistics takes the top spot at 29% while railroad, trucking and airlines round off to the next three spots with double-digit allocation each. The product has accumulated nearly $846.7 million in AUM while sees a good trading volume of more than 418,000 shares a day on average. It charges 43 bps in fees and expenses and has a Zacks ETF Rank of 3 or ‘Hold’ rating with a High risk outlook. Medical/Health Care Though the twin attacks of the recent global market rout and Hillary Clinton’s tweet might dampen the bottom lines of the health care companies, the sector is still expected to report solid earnings growth of 8%. This is primarily thanks to solid industry fundamentals, including rising mergers & acquisitions, emerging market expansion, positive demographic trends and innovation of new products. Investors could find the largest and ultra-popular Health Care Select Sector SPDR ETF (NYSEARCA: XLV ) an exciting pick to benefit from the current trends. The fund follows the S&P Health Care Select Sector Index, holding 57 stocks in its basket. It is largely concentrated on the top two firms – Johnson & Johnson (NYSE: JNJ ) and Pfizer (NYSE: PFE ) – at 10.3% and 8%, respectively. Other firms hold less than 5.7% of assets. Pharma accounts for 38.8% share from a sector look, followed by biotech (24.3%), health care providers and services (19.4%), and equipment and supplies (13.7%). The fund manages about $13.5 billion in its asset base and trades in heavy volume of more than 11.3 million shares. Expense ratio came in at 0.15% annually. It has a Zacks ETF Rank of 1 or ‘Strong Buy’ with a Medium risk outlook. Construction The housing sector emerged relatively unscathed by the recent global market turmoil, which has hit almost every corner of the investing world. The major strength came from the industry-specific fundamentals such as growing demand for homes and affordable mortgage rates. The sector is expected to post 7.5% earnings growth for Q3. Investors seeking to ride this growth could consider the iShares U.S. Home Construction ETF (NYSEARCA: ITB ) . This fund provides a pure play to the home construction sector by tracking the Dow Jones U.S. Select Home Construction Index. It holds a basket of 41 stocks with double-digit allocation going to D.R. Horton (NYSE: DHI ) and Lennar (NYSE: LEN ). Homebuilding takes the top spot at 64.6%, followed by 14.9% in building products and 9% in home improvement retail. The product has amassed $2.1 billion in its asset base and trades in heavy volume of around 3.7 million shares a day on average. The ETF charges 43 bps in annual fees and has a Zacks ETF Rank of 2 with a High risk outlook. Financials This sector also offers opportunities of healthy returns to investors this earnings season with an expected earnings growth rate of 7.5%. Better expense management, rising fees from surging M&A activity, lower litigation charges, solid loan growth, steadily improving credit quality, growing trading businesses and improving balance sheets are fueling optimism in the broad sector. A broad way to play this trend is with Financial Select Sector SPDR ETF (NYSEARCA: XLF ) , having AUM of $17.1 billion and average daily volume of around 35 million shares. The ETF tracks the S&P Financial Select Sector Index, holding 90 stocks in its basket. The top three firms – Berkshire Hathaway (NYSE: BRK.B ), Wells Fargo (NYSE: WFC ), and JPMorgan Chase (NYSE: JPM ) – account for over 8% share each while other firms hold less than 5.8% of assets. In terms of industrial exposure, banks take the top spot at 36.3% while insurance, REITs, capital markets and diversified financial services make up for double-digit exposure each. The fund charges 15 bps in annual fees and has a Zacks ETF Rank of 2 with a Medium risk outlook. Link to the original post on Zacks.com

HACK Or CIBR? Choosing A Cybersecurity ETF

Summary HACK is the more expensive fund, with greater liquidity and more of a pure play portfolio. CIBR is the cheaper fund with greater exposure to larger companies, resulting in slightly less volatility. HACK and CIBR have proven to be considerably more volatile than the broader technology sector. High-profile data breaches have affected companies like Ashley Madison, Sony (NYSE: SNE ), Starbucks (NASDAQ: SBUX ) and Target (NYSE: TGT ). There have also been reports of cyberattacks against government agencies, including the Department of Defense. Organizations around the world are stepping up their efforts to update their protocols and technology to restrict unauthorized intrusions and the theft of sensitive information. As a result, analysts expect spending on cybersecurity to be a growing line item for all manner of organizations. This has led to increased interest in cybersecurity-related stocks and in 2015, cybersecurity stocks had produced some of the market’s best year-to-date returns before the August sell-off. Rather than trying to single out individual firms, two exchange-traded funds, the PureFunds ISE Cyber Security ETF (NYSEARCA: HACK ) and the First Trust Nasdaq CEA Cybersecurity ETF (NASDAQ: CIBR ), offer investors broad exposure and diversification across this niche in the information technology industry. PureFunds ISE Cyber Security Established in November 2014, HACK was the first ETF created to track the cybersecurity industry. The fund’s goal is to provide investment returns that generally correspond to those of the ISE Cyber Security Index before fees and expenses. The index tracks the performance of domestic and international companies that provide cybersecurity or for which cybersecurity is a key driver in their overall business model. The $1.29 billion fund has a 71.5 percent exposure to domestic stocks and a 28.5 percent allocation to foreign securities, mainly Greater Europe and the Middle East. The fund’s largest exposure is to mid-, small- and micro-cap companies. First Trust Nasdaq CEA Cybersecurity The First Trust Nasdaq CEA Cybersecurity ETF began trading on July 7, 2015. CIBR seeks to replicate the performance, before fees and expenses, of the Nasdaq CEA Cybersecurity Index. The benchmark index includes common stocks and depository receipts of companies classified as engaging in cybersecurity according to the Consumer Electronics Association (CEA). The fund intends to hold a position in each security contained within the index. CIBR has a 28 percent allocation to large cap stock as well as a 38 percent allocation to mid-cap and 22 percent exposure to small-cap stocks. CIBR has a 67 percent exposure to domestic securities and a 33 percent exposure to foreign issues, mainly the United Kingdom, the Middle East and Emerging Asia. Fund Differences Although the funds have similar goals, there are differences between the two ETFs. These subtle nuances may result in one fund, rather than the other, being more suitable for your individual portfolio. The first difference is the construction of their underlying benchmark indices. HACK utilizes the ISE Cyber Security Index as its benchmark. This index focuses on companies that develop hardware and software for safeguarding networks, websites and files. CIBR tracks the Nasdaq CEA Cybersecurity Index, which includes companies engaged in building, implementing and managing security protocols for public and private networks, computers and mobile devices. While the indices are similar, they differ in the size of the companies held within the portfolio, their market liquidity and the manner in which the index is weighted. CIBR has a market cap minimum of $250 million and an average three-month trading volume of $1 million. HACK lowers the market cap requirement to $100 million and does not have a trading minimum. While the ISE Cyber Security Index of HACK uses a modified equal weighting methodology, the Nasdaq CEA Cybersecurity Index backing CIBR utilizes a modified liquidity-weighted technique. The result of these differences is HACK has more assets in smaller companies that are more easily categorized as pure plays in the industry. This focus creates the potential for higher volatility and risk associated with owning small and micro-cap stocks. A second difference is portfolio composition. The top five holdings for HACK are Fortinet (NASDAQ: FTNT ), Imperva (NYSE: IMPV ), Trend Micro ( OTCPK:TMICY ), Proofpoint (NASDAQ: PFPT ) and Juniper Networks (NYSE: JNPR ). CIBR’s top holdings include Qihoo 360 (NYSE: QIHU ), Palo Alto Networks (NYSE: PANW ), Cisco (NASDAQ: CSCO ), FireEye (NASDAQ: FEYE ) and NXP Semiconductors (NASDAQ: NXPI ). With a heavier tilt towards software names, HACK is more of a pure play. Overall, HACK has a little over 10 percent of its portfolio in stocks not held in CIBR, while CIBR has about a third of its holdings in stocks not held by HACK. Beyond owning a more differentiated portfolio, CIBR is a bit more diversified since it has more individual holdings within its portfolio. Due to the size of the industry and the companies available for investment, both funds also hold some large caps to fill out their portfolios. As a result, both funds hold large caps such as Cisco Systems and Juniper Networks that are not pure plays on cybersecurity. CIBR doesn’t have a long history and has tracked closely with HACK since inception. Since the inception of HACK, it has outperformed the Technology Select Sector SPDR ETF (NYSEARCA: XLK ), 0.60 percent gain versus a 2.10 percent loss for XLK through September 30, but it comes with a high degree of volatility. In September, XLK fell 1.4 percent, but HACK and CIBR fell 6.9 percent and 3.6 percent, respectively. Since the inception of CIBR in July 2015, XLK is down 1.7 percent, versus a 15 percent drop in HACK and a 12.5 percent decline in CIBR. The recent negative returns may be a reflection of the downturn in the overall market rather than the cybersecurity industry, but it reflects the type of volatility investors can expect. The chart below shows XLK in black. The red line shows the price ratio of HACK versus XLK (a rising line indicates outperformance), while the blue line shows the price ratio of HACK versus CIBR. (click to enlarge) With a short history, one cannot make a long-term prediction about relative performance, but to date, the funds are behaving as expected given their construction. When the technology sector is rising, HACK outperforms XLK. When the technology sector is falling, HACK and CIBR underperformed. HACK also underperformed CIBR when the technology sector declined. Outlook HACK’s emphasis on smaller, faster-growing firms makes it more of a pure play on this market niche. Smaller cap stocks often provide better returns during bull markets and worse returns during a bear market and thus far, performance has been as expected. Investors in cybersecurity stocks can look forward to a roller coaster ride, but HACK will likely deliver bigger gains and losses. By concentrating on larger companies due to stricter liquidity requirements, and greater diversification, CIBR focuses on more established names that may make the ETF better suited for more conservative investors – although even CIBR will be far more volatile than the average technology fund. With an expense ratio of 0.60 percent, CIBR also has a lower cost than the 0.75 percent expense ratio of HACK. Weighing the two options, HACK is the better choice for aggressive investors looking for as much pure play exposure as possible as well as more short-term oriented trades. CIBR would be a little better fit for an investor looking to shift some technology exposure into cybersecurity, if only for the lower expense ratio compared to HACK. Both funds have more than adequate daily volume, but HACK has more than 10 times the daily dollar volume of CIBR, making it the more liquid option for large investors.

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