Tag Archives: nysearcaspy

Is The Acceleration Factor A Better Way To Measure Momentum?

Momentum has received a lot of attention in the asset-pricing literature over the past several decades, and for good reason. Trending behavior is a staple in markets. In contrast with other pricing “anomalies,” short-term return persistence – positive and negative – is a robust factor across asset classes. The fact that momentum is deployed far and wide in the money management industry and hasn’t been arbitraged away suggests that the persistence factor is persistent. The question is whether momentum as traditionally defined can be enhanced? Yes, according to a small but growing corner of research that looks at price trends through an “acceleration” lens. Momentum is generally defined as the directional bias for asset returns to persist, particularly over a 6- to 12-month period. The modern age of momentum research begins with Jegadeesh and Titman’s 1993 study “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Fast forward to the present and you’ll find a small library of research that extends the analysis in a variety of directions, including the recent focus on the so-called acceleration factor. There are several ways to define acceleration, but the general concept is simply a methodology for measuring changes in momentum – “the first difference of successive returns,” as a recent paper explained ( “The Acceleration Effect and Gamma Factor in Asset Pricing” ). What’s the value of monitoring and measuring acceleration? This study finds that it provides “better performance and higher explanatory power than momentum.” As such, “momentum can be considered an imperfect proxy for acceleration.” That’s an intriguing comment since momentum is already viewed as a solid framework as a risk factor and as the raw material for profitable trading strategies. But can we squeeze even more from this realm of asset-pricing analytics in the search for robust signals? Perhaps. Another line of research along these lines comes to us by way of Morningstar, which recently published an academic study that found that acceleration is quite useful for anticipating severe market losses. ” The Economic Value of Forecasting Left-Tail Risk ” reports that the geometric return for the most recent six-month period less its equivalent over the preceding six months, along with trailing 1-year return, are powerful factors for predicting negative skewness in returns. The results suggest, according to the authors, “that it is possible to reduce tail risk without giving up returns.” There are a number of variations one could devise in trying to mine acceleration as a risk metric. David Varadi has explored several possibilities, including what he labels the volatility of acceleration (VOA). Noting that this indicator has interesting properties for estimating volatility and adjusting asset weights, he writes that “the VOA framework is one step in the direction of looking at alternative and possibly better measures of volatility.” The research on acceleration and its applications is still in its infancy, but the early efforts certainly look intriguing. It’s premature to abandon momentum in favor of acceleration. But there’s a compelling case for expanding the definition of price persistence.

Technically Speaking: The Real Value Of Cash

With the ” inmates running the asylum ” during a holiday-shortened trading week, the upward bias to the market is set to continue. However, as I addressed last week: ” As we progress through the last two months of the year, historical tendencies suggest a bias to the upside . This is particularly the case given the weakness this past summer which has left many mutual and hedge funds trailing their benchmarks. The need to play ‘catch-up’ will likely create a push into larger capitalization stocks as portfolios are ‘window dressed’ for year end reporting . This traditional ‘Santa Claus’ rally, however, does not guarantee the resumption of the ongoing ‘bull market’ into 2016. The chart below lays out my expectation for the market through the end of the year. ” (click to enlarge) ” With the markets currently oversold on a very short-term basis, the current probability is a rally into the ‘Thanksgiving’ holiday next week and potentially into the first week of December . As opposed to my rudimentary projections, the push higher will likely be a ‘choppy’ advance rather than a straight line. ” So far, the analysis over the last several weeks has continued to play out as expected. However, and this is crucially important, a near-term expectation of a bullish advance due to the recent correction and seasonal tendencies is not the same as long-term bullish outlook . As stated above, while seasonality likely holds the cards through the end of this year, projecting much beyond that window is foolishness. The Real Value Of Cash This brings to mind a call I had on the radio show recently discussing his advisor’s reluctance to hold cash . The argument against holding cash goes this way: ” If you hold cash, you lose value over time to inflation .” This is a true statement if you hold cash for an EXTREMELY long period. However, holding cash as a ” hedge ” against market volatility during periods of elevated uncertainty is a different matter entirely. As I discussed previously: ” I have written previously that historically it is relatively unimportant the markets are making new highs. The reality is that new highs represent about 5% of the markets action while the other 95% of the advance was making up previous losses. ‘ Getting back to even’ is not a long-term investing strategy . ” (click to enlarge) In a market environment that is extremely overvalued, the projection of long-term forward returns is exceedingly low. This, of course, does not mean that markets just trade sideways, but in rather large swings between exhilarating rises and spirit-crushing declines. This is an extremely important concept in understanding the “real value of cash.” (click to enlarge) The chart below shows the inflation-adjusted return of $100 invested in the S&P 500 ( using data provided by Dr. Robert Shiller ). The chart also shows Dr. Shiller’s CAPE ratio. However, I have capped the CAPE ratio at 23x earnings which has historically been the peak of secular bull markets in the past. Lastly, I calculated a simple cash/stock switching model which buys stocks at a CAPE ratio of 6x or less and moves back to cash at a ratio of 23x . I have adjusted the value of holding cash for the annual inflation rate which is why during the sharp rise in inflation in the 1970s, there is a downward slope in the value of cash . However, while the value of cash is adjusted for purchasing power in terms of acquiring goods or services in the future, the impact of inflation on cash as an asset with respect to reinvestment may be different since asset prices are negatively impacted by spiking inflation. In such an event, cash gains purchasing power parity in the future if assets prices fall more than inflation rises. (click to enlarge) While no individual could effectively manage money this way, the importance of “cash” as an asset class is revealed. While cash did lose relative purchasing power, due to inflation, the benefits of having capital to invest at lower valuations produced substantial outperformance over waiting for previously destroyed investment capital to recover. While we can debate over methodologies, allocations, etc., the point here is that ” time frames ” are crucial in the discussion of cash as an asset class. If an individual is “literally” burying cash in their backyard, then the discussion of the loss of purchasing power is appropriate. However, if cash is a “tactical” holding to avoid short-term destruction of capital, then the protection afforded outweighs the loss of purchasing power in the distant future. Much of the mainstream media will quickly disagree with the concept of holding cash and tout long-term returns as the reason to just remain invested in both good times and bad. The problem is that it is YOUR money at risk. Furthermore, most individuals lack the ” time ” necessary to truly capture 30- to 60-year return averages. For individuals, trying to save for their retirement, there are several important considerations with respect to cash as an asset class: Cash is an effective hedge against market loss. Cash provides an opportunity to take advantage of market declines. Cash provides stability during times of uncertainty (reduces emotional mistakes) Importantly, I am not talking about being 100% in cash. I am suggesting that holding higher levels of cash during periods of uncertainty provides both stability and opportunity. With the fundamental and economic backdrop becoming much more hostile toward investors in the intermediate term, understanding the value of cash as a ” hedge ” against loss becomes much more important. As John Hussman recently noted: ” The overall economic and financial landscape, then, is one where obscene valuations imply zero or negative S&P 500 total returns for more than a decade – an outcome that is largely baked-in-the-cake regardless of shorter term economic or speculative factors. Presently, market internals remain unfavorable as well. Coming off of recent overvalued, overbought, overbullish extremes, this has historically opened a clear vulnerability of the market to air-pockets, free-falls and crashes. ” As stated above, near zero returns do not imply that each year will have a zero rate of return. However, as a quick review of the past 15 years shows, markets can trade in very wide ranges leaving those who ” rode it out ” little to show for their emotional wear. Given the length of the current market advance, deteriorating internals, high valuations and weak economic backdrop; reviewing cash as an asset class in your allocation may make some sense. Chasing yield at any cost has typically not ended well for most. Of course, since Wall Street does not make fees on investors holding cash, maybe there is another reason they are so adamant that you remain invested all the time. Just something to think about.

Portfolio Optimization With Leveraged Bond Funds

Summary Bond funds are great because they generate alpha and usually have negative correlation with stocks. Using the leveraged version of a bond fund can drastically improve portfolio optimization (i.e. produce greater expected returns for a given level of volatility). I use SPY/TLT and SPY/TMF to illustrate. SPY/TLT Portfolio Optimization Consider a two-fund portfolio optimizaton problem based on the SPDR S&P 500 ETF Trust (NYSEARCA: SPY ) and the iShares 20+ Year Treasury Bond ETF (NYSEARCA: TLT ). Often the goal is to maximize the ratio of expected returns to volatility (Sharpe ratio). I don’t like that approach, because when you maximize Sharpe ratio, you tend to get a portfolio with great risk-adjusted returns but relatively small raw returns. Instead, let’s say the goal is to choose an asset allocation that maximizes expected returns for some level of volatility that you can tolerate. A good way to do that is to look at a plot of mean vs. standard deviation of daily returns for various asset allocations. Here is that plot using SPY and TLT data going back to 2002. (click to enlarge) The red curve shows mean and standard deviation of daily portfolio gains for various asset allocations. The points represent 10% asset allocation increments. The top-right point is 100% SPY, 0% TLT; the next point is 90% SPY, 0% TLT; and so on until the bottom-most point on the other end of the curve, which is 0% SPY, 100% TLT. Suppose you want no more than three-fourths the volatility of SPY, or a standard deviation no greater than 0.93%. Looking at the graph, we want to be right around the third data point from the upper-right end of the curve. That data point represents 80% SPY, 20% TLT. This is the optimal allocation for an investor who wants to maximize returns at three-fourths the volatility of SPY. SPY/3x TLT Portfolio Optimization Let’s see how replacing TLT with a perfect 3x daily TLT fund (no expense ratio, no tracking error) affects the portfolio optimization problem. (click to enlarge) The red curve shows the same data as in the first figure, it just looks different because I had to zoom out to accommodate the SPY/3x TLT curve. Here I show asset allocations in 5% increments for the blue curve. The lowest point on the blue curve is 100% SPY, 0% 3x TLT; the next point is 95% SPY, 5% 3x TLT; and so on until the rightmost point, which is 0% SPY, 100% 3x TLT. Interestingly, increasing 3x TLT exposure from 0% reduces volatility and increases mean returns up until about 25% 3x TLT. Over the volatility range 0.884%-1.235%, you can do substantially better in terms of maximizing mean returns for a given level of volatility with SPY/3x TLT compared to SPY/TLT. Going back to the first example, at a volatility of 0.93%, or three-fourths the volatility of SPY, the best mean return you can achieve with SPY/TLT is 0.039%, with 80.1% SPY and 19.9% TLT. The best you can do with SPY/3x TLT is 0.059%, with 65.5% SPY and 34.5% 3x TLT. Daily returns of 0.059% and 0.039% correspond to CAGRs of 16.0% and 10.3%, respectively. For another interesting special case, suppose you can tolerate the volatility of SPY. With SPY/TLT, the optimal portfolio is 100% SPY and 0% TLT, with a mean daily return of 0.040%. With SPY/3x TLT, the optimal portfolio is 48.4% SPY and 51.6% 3x TLT, with a mean daily return of 0.069%. Also noteworthy is the fact that SPY/3x TLT portfolios are capable of achieving volatility greater than SPY, while SPY/TLT portfolios are not. This could be appealing to aggressive investors. A Real 3x Bond Fund: TMF So far, I’ve shown that a perfect 3x daily TLT fund would be extremely useful for portfolio optimization. The next question is whether such a fund exists, and how “perfect” it is in regard to expense ratio and tracking error. There are a few options, but I think the most relevant is the Direxion Daily 20+ Year Treasury Bull 3x Shares (NYSEARCA: TMF ). TMF was introduced on April 16, 2009, and has a net expense ratio of 0.95%. The next figure shows that indeed TMF effectively multiplies daily TLT gains by a factor of 3. The correlation between actual TMF gains and 3x TLT gains over TMF’s 6.5-year lifetime is 0.996. (click to enlarge) I realize that TMF does not attempt to track 3x TLT, but rather 3x the NYSE 20 Year Plus Treasury Bond Index (AXTWEN). But practically speaking TMF operates very much like a 3x TLT ETF. Let’s go ahead and re-examine the mean vs. standard deviation plot for SPY/TLT, SPY/3x TLT, and SPY/TMF over TMF’s lifetime. (click to enlarge) This is interesting, and slightly disappointing. As in the previous plot, we see that SPY/3x TLT achieves drastically better mean returns for particular levels of volatility compared to SPY/TLT. The orange curve for SPY/TMF is also higher than SPY/TLT, but not as much so as SPY/3x TLT. It seems that TMF’s reasonable expense ratio and tiny tracking error do detract somewhat from the optimization problem. But we still see that increasing exposure to TMF from 0% to about 20% reduces volatility and increases expected returns, and SPY/TMF does much better than SPY/TLT for those who can tolerate volatility between 0.722% and 1.022%. Leveraged Bond Funds Multiply Alpha and Beta As I’ve argued in other articles (e.g. SPY/TLT and SPXL/TMF Strategies Work Because of Positive Alpha, not Negative Correlation ), the reason bond funds compliment stocks so well is that they generate positive alpha. A bond fund with zero or negative alpha has no place in any portfolio; you would be better off using cash to adjust volatility and expected returns. Anyway, bond funds are special because they generate alpha. Ignoring tracking error and expense ratio, a leveraged version of a bond fund multiples both the alpha and beta of the underlying bond index. We can see this with TLT and TMF. Over TMF’s lifetime, their alphas are 0.061 and 0.173, and their betas are -0.492 and -1.493, respectively. TMF’s alpha is 2.84 times that of TLT’s, and its beta is 3.03 times that of TLT’s. 3x greater alpha does not immediately render 3x TLT the better choice for portfolio optimization. You have to look at the effect on both expected returns and volatility, which are both functions of alpha and beta. Suppose you can achieve the same portfolio volatility with c allocated to SPY and (1-c) to TLT, or with d allocated to SPY and (1-d) to 3x TLT. If you subtract the expected return of the SPY/TLT portfolio from the expected return of the SPY/3x TLT portfolio, you get: (d-c) E[X] + [3(1-d) – (1-c)] E[Y] where X represents the daily return of SPY, and Y the daily return of TLT. Whether this expression is positive or negative depends on d, c, E[X], and E[Y] (which can also be expressed as alpha + beta E[X]). For SPY and TLT, the expression is always positive, which means that SPY/3x TLT provides better expected returns than SPY/TLT for any level of volatility that both can achieve. Conclusions Leveraged bond funds appear to be extremely useful for portfolio optimization. In the case of SPY and TLT, we saw that using a 3x version of TLT, like TMF, allows us to: Improve expected returns for a particular level of volatility. Achieve the same volatility as SPY, but with drastically better expected returns. Take on extra volatility beyond SPY’s in pursuit of greater raw returns. In practice, TMF’s expense ratio and tracking error detract somewhat from the performance of an ideal SPY/3x TLT portfolio. But SPY/TMF still allows for substantial improvements over SPY/TLT in terms of maximizing returns for a given level of volatility.