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ETF Update: Smart Beta Launches As Far As The Eye Can See

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 other week (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. As you might have noticed from the title, smart beta funds were on my mind this week. This might have something to do with the last 8 launches falling into that self-proclaimed category. It might also be due to a great read from Abnormal Returns, ” Finance blogger wisdom: smart beta bubble? ” In the linked article the author presented the following question to his online peers: The ‘smart beta’ or factor-investing bubble seems to be in full bloom. Is ‘smart beta’ simply the new active investing? If so, what happens to the entire fund industry which was built on the high fees associated with active management? This is a question that many have also covered on Seeking Alpha, but the most recent example is from Benjamin Lavine, CFA , whose article was posted on Wednesday (3/30). I would highly recommend it for any readers wondering what is behind the smart beta trend and how to interpret the term when considering an investment. With that disclaimer aside, let’s jump into the most recent round of smart beta launches: Fund launches for the week of March 21st, 2016 Principal expands into smart beta (3/22): The Principal Price Setters Index ETF (NASDAQ: PSET ) and the Principal Shareholder Yield Index ETF (NASDAQ: PY ) are the first smart beta launches from Principal Funds; both target mid- and large-cap domestic firms. However, PSET “focuses on companies with sustainable pricing power, consistent sales growth, high/stable margins, quality earnings, low leverage, and high levels of profitability,” while PY is for investors more concerned with “sustainable shareholder yield, strong cash flow generation, and capacity to increase dividends and/or buybacks.” Both funds are a relatively large departure from the Principal EDGE Active Income ETF (NYSEARCA: YLD ), which was launched in July 2015. This first venture into ETFs is an active fund investing across multiple income-producing asset classes in search of high-income investments. Victory Capital Management rolls out an emerging market fund (3/23): The Victory CEMP Emerging Market Volatility Wtd Index ETF (NASDAQ: CEZ ) was the third smart beta launch of the week. The in-house CEMP Emerging Market 500 Volatility Weighted Index “combines fundamental criteria with volatility weighting to seek to improve an investor’s ability to outperform traditional indexing strategies.” It is worth noting that the top countries represented at this time are Taiwan, China, South Korea and India; all of which are still considered emerging by MSCI , but many have argued that they are quickly evolving out of the traditional definition. Fund launches for the week of March 28th, 2016 Fund closures for the weeks of March 21st and 28th, 2016 Direxion Value Line Conservative Equity ETF (NYSEARCA: VLLV ) Direxion Value Line Mid- and Large-Cap High Dividend ETF (NYSEARCA: VLML ) Direxion Value Line Small- and Mid-Cap High Dividend ETF (NYSEARCA: VLSM ) ALPS Sector Leaders ETF (NYSEARCA: SLDR ) ALPS Sector Low Volatility ETF (NYSEARCA: SLOW ) ALPS STOXX Europe 600 ETF (NYSEARCA: STXX ) Global Commodity Equity ETF (NYSEARCA: CRBQ ) iSharesBond 2016 Corporate Term ETF (NYSEARCA: IBDA ) iSharesBond 2016 Corporate ex-Financials Term ETF (NYSEARCA: IBCB ) 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. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

Investment Strategy: When To Sell A Stock?

By Rupert Hargreaves Deciding when to sell a stock is often a more complicated process than buying it in the first place. Indeed, holding onto a loser for too long can severely curtail long-term returns. The same can be said if you hold onto a winner for longer than needs be as a sudden shift in market sentiment might see the majority of your gains erased. With this being the case, refining your selling process is a vital part of developing your investment strategy. This is a topic the February 29 issue of Value Investor Insight looks at in an interview with Danny Bubis, Ben Ellis, Jay Hedstrom and Amar Pandya of Tetrem Capital Management , which has produced an annualized return for investors of 8.9% since 1997, vs. 7.1% for the S&P 500. When To Sell A Stock? Investment strategy: When To Sell A Stock? Tetrem seeks out companies using a value approach: beaten-down stocks reflecting an unwarranted pessimism over the persistence and sustainability of their businesses. Of course, the selling process starts when the fund first buys an investment and research on each company is focused on modelling each potential investment’s fair value on the basis of normalised earnings in the base case, bull case, and bear case and the justified multiple for earnings in each of those scenarios. When these scenarios have been calculated, the fund’s analysts assign probability weightings to each case, and then use this probability weighting to calculate the potential upside the security. Generally speaking, the fund is looking for $3 of upside for every $1 of downside. Why does Tetrem Capital use a probability-weighted fair value calculation? Well, according to Danny Bubis this approach helps the fund better frame things in terms of risk versus reward and results in better investment decisions. When it comes to selling, Tetrem’s team has decided to refine their selling process after observing that many of the fund’s missteps have involved sticking with losers too long or not letting winners run long enough. To counter these mistakes, the fund’s team is making a more concerted effort to have high conviction buys push out more marginal ideas. The key test here: if the stock in question fell 10% to 20%, would the fund step in and aggressively buy more? If the answer is no, then there could be better ideas out there. Another rule the fund has introduced is that when something happens, which puts the original investment thesis at risk, the weighting in the fund is immediately reduced to 1.5%, a normal weight the fund is around 3% – no matter what the stock price does. These two parts of the firm’s investment strategy help Tetrem manage the downside; when it comes to the upside, the fund also has a rule in place to ensure that it does not get caught out by letting a winner run too long. Upside management technique Tetrem’s upside management or profit taking method is based on its fair value probability calculation. In the interview with Value Investor Insight, one of the fund’s current positions, Microsoft (NASDAQ: MSFT ) is used as an example. Originally, Tetrem acquired Microsoft when it was a beaten down by the market due to its entrenched management, reliance on PC and weakness in consumer markets. However, over the past two years, the company has transformed itself and successfully adapted to a mobile-first, cloud-first world. The stock is up 100% in five years, excluding dividends and Tetrem’s probability fair value estimate has increased alongside the stock price, as the company has grown and developed with the market, the probability of the bull case is higher, and the probability of the bear case is lower. This floating fair value probability estimate helps Tetrem’s team stick with compounders longer than it might have done without the floating calculation. Disclosure: Rupert may hold positions in one or more of the companies mentioned in this article.

How Long Should I Give An Investment Plan?

Even the most brilliantly crafted investment plan has to be given time to work. The markets are inherently volatile but also inherently profitable. And when you start investing in the markets, you are very likely to see many highs and lows as the market gyrates before you see permanent gains. And since asset allocation involves crafting a portfolio out of many sectors which have low correlation, one component of your portfolio certainly will experience an early loss. Diversification means you will always have something to complain about. Perhaps the most important part of implementing an investment plan is the wisdom to know when one category doing poorly means you should do something and when it means nothing. We know from behavioral finance that many people give up on a brilliant investment philosophy too soon. They chase returns rather than rebalancing. And we know from studies on mutual fund flows that investors underperform the very mutual funds they are invested in because they buy funds after they have gone up and they sell funds after they have gone down. We don’t want to be the foolish investor who sells at the bottom only to reinvest at the top of the next bubble. Here is the primary question to help you discriminate between a brilliant investing strategy and a mistake: Do you have sufficient data to justify the long-term mean returns you want? It is a mistake to select an investment sector based on recent returns. In order to get meaningful statistics, you need to use the longest time horizon possible. Even 30 years is not long enough to judge which investment will have a higher mean return for the next 30 years. For example, we recently had a 30-year time period where long-term bond returns beat the return for stocks . Periodically, it is wise to reevaluate your investment selection to see if you made a mistake. You may have been enamored by the ability of a fund manager to select stocks . You may have thought a fund was worth higher fees and expenses. You may not even have understood what you were investing in. You may have invested in something that has a low or even negative mean return. Or you may have invested in an illiquid asset. If you do find a mistake, it is always a good time to sell a bad investment. There is no reason to “wait for a rebound,” because a better investment will on average rebound better for you. During the portfolio construction process, look for sectors with a high expected return, a low volatility, and a low correlation with other components of your portfolio. Then, when you experience the volatility, ask yourself if it behaved as you expected. Imagine that you have invested in a fund tracking the S&P 500 Index and it quickly experienced over two years a -19% annualized loss. Wondering if you made a mistake, you ask yourself, did your experience fit what your data expected? To answer this question, you look at the range of returns experienced by the S&P 500 Index since 1928 (all the data we have). The mean return (not including dividends) is about 7%. In the graph below, you can see this as the graph funnels around a 7% return the longer the number of years. The thick bars are 1-standard deviation from that mean; the thin bars are two standard deviations. Click to enlarge Returns within one or two standard deviations are commonplace returns. The data doesn’t just expect these, it predicts them. Within one standard deviation of the mean are approximately two out of every three returns experienced. Meanwhile, approximately 22 out of every 23 returns are within two standard deviations. As you can see, it depends on the number of years how wide the range of predicted annualized returns. Over a one-year time period, one standard deviation from the mean is from -13.00% to 28.07%. Meanwhile, over a thirty-year time period, one standard deviation from the mean is 5.45% to 8.53%. Two standard deviations for one-year time periods is -33.53% to 48.06%, and for thirty-year time periods, it is 3.91% to 10.08%. When you look at two-year time periods, the two-standard-deviation set of returns is from -21.81% to 34.56%. The return you experienced, -19%, falls in this time period, making it commonplace. Your data not only expected it, your data predicted it. Despite one-, two-, and three-year time periods all having moderate annualized losses within one-standard deviation, for the S&P 500 Index at a 7-year holding period, the bottom of the one-standard deviation range (2 out of every 3 returns experienced) rises above zero to a positive 0.02%. The bottom of the two-standard deviation range (22 out of every 23 returns) rises above zero after a 19-year period. Even good indexes which are part of a carefully crafted portfolio on the efficient frontier have a bad decade. Get rid of them at the low and you are liable to miss the recovery as the index returns revert to the mean and have some greater than average growth. And while individual stocks can go to zero, broad indexes cannot. To ensure this fact, your funds should be comprised of a large number of holdings. There is no such thing as over diversification. A large number of holdings helps ensure that the category is worth a place in your asset allocation for the long term even when returns are below average for a period of time. There are reasons to remove a sector from your asset allocation, but not simply for returns that are below average. The opposite is true, however. When a category experiences rapid appreciation, investors piling in may cause the price to rise faster than the expected earnings. A higher than normal forward P/E ratio can be an indicator of lower than expected future returns. Dynamic asset allocation would suggest trimming the allocation to sectors with a higher forward P/E ratio so that when the sector reverts to the mean, you have less experiencing the fall. Sometimes even a good investment can drop precipitously. Approximately 1 out of every 23 times the stock market will experience returns greater than two standard deviations from the mean. The markets are more abnormal than a normal Gaussian bell curve. This non-Gaussian mathematics is called Power Laws and forms the basis for fractals. Stock returns experience 4 or more standard deviations greater than normal statistics would predict. Gaussian statistics experience greater than 3 standard deviations approximately 0.2% of the time whereas the stock market experiences greater than 3 standard deviations approximately 0.56% of the time . When returns are outside of two standard deviations, the same analysis applies, but the hype from the financial news media is terrifying. The worst 12-month return for the S&P 500 was -70.13% (a 4-standard deviation loss) and ended June 30, 1932. The best 12-month return ended just 12 months later and was 146.28% (a 7-standard deviation gain). I take comfort in the fact that unusually large drops are often followed by unusually large gains. A similar pairing happened during the crash of 2008. The 12 months prior to 2/28/2009 experienced a -44.76% drop (a 3-standard deviation loss). The next 12 months appreciated 50.25% (a 3-standard deviation gain). For the most part, short-term returns should not ruin a brilliant long-term investment strategy. Normally, it is best to rebalance your portfolio selling what has gone up and buying what has gone down. If you can’t stomach rebalancing your portfolio, at least don’t lose heart and abandon the plan.