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In Which I Answer A Question About The Volatility ETNs

The prevailing wisdom on the volatility ETNs, VelocityShares Daily Inverse VIX Short-Term ETN (NASDAQ: XIV ) and iPath S&P 500 VIX ST Futures ETN (NYSEARCA: VXX ), is that XIV will rise over time and VXX will fall as long as the term structure is in contango more often than it’s in backwardation. A recently elapsed period, slightly longer than a year, makes apparent that’s not the case. Over the period from 2-Mar-2015 to 18-Mar-2015, both XIV and VXX experienced substantial net losses. VXX declined -27.5%, while XIV declined -29.9% (Figures 1 and 2). Figure 1. XIV prices Figure 2. VXX prices This loss for both ETNs over a prolonged period occurred while the term structure was in contango 73% of the time – 2.7X more often than it was in backwardation, as Figure 3 shows below. Why is that? Click to enlarge Figure 3. Percent Contango from 2-Mar-2015 to 18-Mar-2016 One way to answer this question is by reference to variance drain. I picked the period 2-Mar-2015 to 18-Mar-2015 for illustration purposes in this article because it happens that the average of percent daily returns over this period is very close to zero for both ETNs. You can see that in Figure 4 below, which shows running totals for the percent daily returns for the indexes of both ETNs. Running totals for each end at zero, which of course means that the average percent daily return was also zero. Click to enlarge Figure 4. Running total of daily percent changes. The concept of variance drain was introduced by Tom Messmore in the context of comparing investment advisors based on average yearly percent returns. In brief, average periodic returns is a mathematically incorrect basis for comparison, since percentage gains accrue multiplicatively, not additively. This is best explained by example. Suppose you invest $100 in asset X. On Day 1, its market value falls by 25%. However, on Day 2, it rises by 25%. The average daily rate of return is (-25% + 25%)/2 = 0%. But your investment has not returned to its original value. Instead, it is now worth: $100*(1-0.25)*(1+0.25) = $93.75 A 6.25% loss. Since multiplication is commutative, order doesn’t matter. Investment Y that performs inversely to investment X, gaining 25% on Day 1, then losing 25% on Day 2 will also lose 6.25%. In general, this can be expressed as: I 0 *(1-α)*(1+α) = I 0 -α 2 , where I 0 is the initial investment. Clearly, the larger α is, the greater the net loss. Note that variance drain is not an actual loss. There’s no counterparty to variance drain. Nor is it a frictional drag in the sense that fees or leverage cost are. Rather it’s a demonstration that average periodic returns do not represent longer-term returns over multiple periods. In the case of the volatility ETNs XIV and VXX, the inverse relationship of their daily percent returns simply does not carry over to longer time periods, except by chance. What this means is that the question of why both XIV and VXX lost value, which several readers have raised in the comment sections of recently published articles on the volatility ETNs, is only a question if one starts from an incorrect assumption – namely that XIV and VXX are inversely correlated over time periods longer than one day. Since they’re not, both may lose value over time. Additionally, during time periods longer than one day when one loses as the other gains, those changes should not be expected to be equal and opposite. It’s also worth noting that excess of contango during this approximately one-year period did not result in XIV gaining value. On the contrary, it lost a substantial amount of its prior value. I’d like to encourage those who trade these ETNs to be certain the risks are well understood. Among those risks is the risk of placing too much faith in axioms and strategies that were formed during a period when the VIX was generally calm and declining. They may not apply during prolonged periods when the VIX is rising or is more frequently spiking. Disclosure: I am/we are long XIV. 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. Additional disclosure: I may initiate or close a long or short position in any of the volatility ETNs over the next 72 hours.

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.