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‘Betterer’ Investing For A Tired Bull Market

Robo-advisors may not be a “betterer” idea than traditional advisors if all you get is a diversified buy-and-hold approach. If you are a long-term buy-and-hold investor, you should be prepared to weather an occasional four- to seven-year period of diminished portfolio value. If that troubles you, perhaps you should rethink your strategy. At age 33, tennis great Roger Federer trains hard to get “betterer” to keep up with the challenges of staying at the top of the ATP World Tour. While watching a match I saw an ad for robo-advisor Betterment, which suggested that their method of diversified, passive investing is better than more costly traditional advisor services. That got me thinking about what is truly better. Mutual funds were created as a better way to invest in a diversified portfolio of assets aimed at a selected market or market segment. ETFs were created as a lower cost way to do what mutual funds do. Robo-advisors were created as a lower cost way to do what human advisors do, largely with mutual funds and ETFs. All of this has been done to drive the cost out of a “buy and hold” diversified investing approach promulgated by Modern Portfolio Theory (MPT). If a relatively higher-priced wealth advisor implementing MPT on your behalf is like smoking an excellent Cuban cigar, then using a robo-advisor is like smoking a generic cigarette. It’s cheaper, but smoking is still bad for you. I’m not going to go into the cascading unrealistic characterizations, false assumptions and inappropriately applied elegant math that underlies MPT. Nor am I going to explain why it has been so feverishly adopted by the financial products and services industry. There is plenty of that around to read, and you can decide for yourself if the king has no clothes. In the end, you should decide if your investing priority is to beat the market (relative performance) or to make steady positive gains (absolute return). It seems to me that most wealth is created by savings and/or by well-timed concentrated investment positions. Every wealthy person I know got that way either because they inherited wealth, or they spent less than they made (saved), or they had a concentrated investment in a corporation where they worked or in their own business. As Warren Buffett once said, ” Diversification is protection against ignorance. It makes little sense for those who know what they are doing.” While there are likely many exceptions to this generalization, there are some important factors working against wealth creation via long term holding of diversified financial assets? They include: The timing of contributions to investment portfolios; Unforeseen periodic major market corrections that put investment portfolios underwater (or in “drawdown”): Repeated reactions to market advances and declines, wherein many investors enter selected markets only after they have risen and exit only after they gave fallen; and The precept of MPT that the trends of market pricing cannot be known, and are therefore not addressed. The inability of most individual investors and their advisors to protect against market downside variation (drawdown) is obfuscated with the marketing of long-term average market return statistics and assertions about the risk-reduction value of diversification. The market cycles and the investor behavior they illicit contribute heavily to low average investor returns . US equities have risen each year since 2009. Since 1871 US equities have never risen for seven consecutive years. Are you betting that 2015 will break the record? Even though there are secular bull and bear markets that can last 20-30 years, there are dramatic cyclical market downturns that occur about twice a decade which can have severe negative impacts on one’s cumulative investment return. It took 30 months for the S&P 500 to fall 49% between March 2000 and October 2002, and about seven years to recover (total return). It took 17 months for the S&P 500 to fall 57% in the 2007-2008 financial crisis and 5.4 years to recover (total return). More significantly, this last recovery has been fueled by central banks flooding markets with liquidity and forcing money out of savings and into risk assets to find a return. How long could your exposure to equities be in drawdown after the next crisis, if “the Fed” (that’s Federal Reserve, not Roger Federer) is not there to bail you out? So, if you are wealthy and trying to protect what you have, or if you are trying to build wealth over time, you should be wary of applying the generic diversified long-only MPT approach, no matter if you are implementing it with a big bank wealth advisor or with a robo-advisor. How much of a drawdown are you comfortable with? How long do you have before you may have to use some of those invested funds? Liquidation of investments in drawdown is permanent wealth impairment. Absolute return may be a better investment goal for many, especially today. Unfortunately, it is not available from an online robo-advisor for 20 basis points a year. The skills and proprietary analysis required for well-timed concentrated investment positions usually comes with higher fees. If it can help you to consolidate the gains you have made since 2009, maybe it is worth a higher fee. After all, it’s not only what you make, but what you keep that matters. And if your forward investment time horizon is likely to include another major market correction, you will keep more (after paying higher fees) by side-stepping most of the drawdown (assuming the advisor times the market well). By putting your faith in a selected absolute return strategy, you may protect yourself against natural inclinations for unprofitable reactions to changing market trends. It is said that markets climb up the stairs, but come down the elevator. Absorbing the full impact of the next major market correction will be a lot more painful than paying a higher advisory fee. Just as the great Federer has to train hard and switch to a new racquet to stay near the top, maybe your forward results will be “betterer” with an absolute return investment approach in this tired bull market. (click to enlarge) Absolute Return, a publication of Hedge Fund Intelligence, maintains a database of equal weighted hedge fund performance separated into 16 strategies. The Composite Index is an equally weighted index which represents the median performance of all funds in the Absolute Return Database. Returns are net of fees. Trendhaven makes no claims regarding the accuracy of the data reported by hedge funds or compiled and reported by Absolute Return or Hedge Fund Intelligence. Additional disclosure: The author is an investment advisor representative applying an absolute return strategy in separately managed accounts.

And The Winner Is…

Until recently, the longest back test using stock market data was Geczy and Samonov’s 2012 study of relative strength momentum called “212 Years of Price Momentum: The World’s Longest Backtest: 1801-2012”. The length of that study has now been exceeded by an 800 year backtest of trend following absolute momentum in Greyserman and Kaminski’s new book, Trend Following with Managed Futures: The Search for Crisis Alpha . The authors looked at 84 equities, fixed income, commodities, and currencies markets as they became available from the years 1200 through 2013. They established long or short equal risk sized positions based on whether prices were above or below their 12-month rolling returns. The annual return of this strategy was 13% with an annual volatility of 11% and a Sharpe ratio of 1.16. Anyone who had doubts about the long-run efficacy of trend following momentum should no longer be doubtful. However, let’s not just look at trend following on its own. Let’s also compare it to other possible risk reducing or return enhancing approaches and see what looks best. We will base our comparisons on the performance of U.S. equities because that is where long-run risk premium and total return have been the highest. We also have U.S. stock market data available from the Kenneth French data library all the way back to July 1926. We will compare trend following to seasonality and then to the style and factor-based approaches of value, growth, large cap, and small cap. We will also see if it makes sense to combine these with trend following. For seasonality, we look at the Halloween effect, sometimes called “Sell in May and go away…” This has been known to practitioners for many years. There have also been a handful of academic papers documenting the positive results of holding U.S. stocks only from November through April. The following table shows the results of this strategy compared with absolute momentum applied to the broad U.S. stock market from May 1927 through December 2014. With 10-month absolute momentum, we are long stocks when the excess return (total return less the Treasury bill rate) over the past 10 months has been positive.[1] Otherwise, we hold Treasury bills. We also hold Treasury bills when we are out of U.S. stocks according to the Halloween effect (in stocks November-April, out of stocks May-October). We see that the 6-month seasonal filter of U.S. stock market returns substantially reduces volatility and maximum drawdown, but at the cost of reducing annual returns by over 200 basis points. Trend following absolute momentum, on the other hand, gives a greater reduction in maximum drawdown than seasonality with almost no reduction in return. There is no reason to consider seasonal filtering when absolute momentum gives a greater reduction in risk without diminished returns. The table below shows the U.S. market separated into the top and bottom 30% based on book-to-market (value/growth) and market capitalization (small/large). We see that value and small cap stocks have the highest returns but also the highest volatility and largest maximum drawdowns. Style US Mkt Value Growth Large Small Annual Return 11.8 16.2 11.3 11.5 16.6 Annual Std Dev 18.7 25.1 18.7 18.1 29.3 Annual Sharpe 0.42 0.46 0.39 0.42 0.41 Maximum DD -83.7 -88.2 -81.7 -82.9 -90.4 Most academic studies ignore tail risk/maximum drawdown, but these can be very important to investors. Not many would be comfortable with 90% drawdowns.[2] On a risk-adjusted basis (Sharpe ratio), neither small cap nor value stocks appear much better than growth or large cap stocks. This is consistent with the latest academic research showing no small size premium and a value premium associated only with micro cap stocks.[3] Let’s now see what happens now when we apply absolute momentum to these market style segments: Style w/Absolute Momentum AbsMom ValAbsMom GroAbsMom LgAbsMom SmAbsMom Annual Return 11.5 13.3 10.3 11.5 13.9 Annual Std Dev 12.9 17.2 13.3 12.5 21.1 Annual Sharpe 0.58 0.53 0.48 0.60 0.46 Maximum DD -41.4 -66.8 -42.3 -36.2 -76.9 In every case, adding absolute momentum reduces volatility, increases the Sharpe ratio, and substantially lowers maximum drawdown. The biggest impact of absolute momentum, however, is on large cap stocks, followed by the overall market index. The use of a trend following absolute momentum overlay further reduces the relative appeal of value or small cap stocks. We may wonder why large cap stocks respond better to trend following. The answer lies in a study by Lo and MacKinlay (1990) showing that portfolio returns are strongly positively autocorrelated (trend following), and that the returns of large cap stocks almost always lead the returns of small cap stocks. Since trend following lags behind turns in the market, investment results should be better if you can minimize that lag by being in the segment of the market that is most responsive to changes in trend. That segment is large cap stocks, notably the S&P 500 index, since they lead the rest of the market. In my book, Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk , I give readers an easy-to-use, powerful strategy incorporating relative strength momentum to select between U.S. and non-U.S. stocks and absolute momentum to choose between stocks or bonds. I call this model Global Equities Momentum (GEM). And what index is the cornerstone of GEM? It’s the S&P 500, the one most responsive to trend following absolute momentum and that gives the best risk-adjusted results. Einstein said you should keep things as simple as possible, but no simpler. One can always create more complicated models or include more investable assets. But as we see here, trend following momentum is best when it is simply applied to large cap stocks. [1] We use 10-month absolute momentum instead of the more popular 10-month moving average because absolute momentum gives better results. See our last blog post, ” Absolute Momentum Revisited “. [2] The next largest maximum drawdown was 64.8 for value and 69.1 for small cap on a month-end basis. Intramonth drawdowns would have been higher. [3] See Israel and Moskowitz (2012) for empirical results. Delisting bias and high transaction costs can also reduce any small cap premium. Results are hypothetical, 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. Please see our Disclaimer page for more information.

Explaining Why The Portfolio-Barbell Works

One classic (liability-driven) portfolio strategy, known for obvious reasons as the “barbell,” entails a lot of very defensive low-beta assets on the one side, and a lot of aggressive high-beta assets on the other. Practitioners follow the advice of Mr. Bing Crosby: they don’t “mess with Mr. In-between.” For the most part, this is a practitioners’ strategy, not a theorists’ strategy. There’s been little reason in theory to think that it should work. After all. if you want diversification, why not include some assets in between those extremes? And if you don’t care for diversification, why not go whole hog with a “bullet” strategy? Despite its lack of conceptual foundations, practitioners continue to use it. Theory in Pursuit of Practice Donald Geman, a Fellow at the Institute of Mathematical Statistics and a professor of Applied Mathematics at Johns Hopkins, with expertise in machine learning, joins with two other scholars in writing a paper, now a preprint at arXiv, which seeks to put a foundation under this practice. The other authors are: Hélyette Geman of the University of London and… Nassim Nicholas Taleb, of black swans and anti-fragility renown. The gist of the paper, expressed non-mathematically, is that managers work with the facts they know. What they know is that they have to constrain the tails of their portfolio-return bell curve to satisfy various regulatory or institutional demands, Value at Risk, Conditional Value at Risk, and stress testing. The “operators,” as the authors call the decision makers in portfolio management, aren’t “concerned with portfolio variations” except insofar as they have “a vague notion of association and hedges.” They set out on the one hand to limit the maximum drawdown with investments at the conservative side of the scale in response to the sort of pressures and mandates just listed, then they move to the other end of the scale to seek to get the upside benefits of the same market uncertainties against which they’ve just protected themselves. In the course of making these points, the authors get in the by-now customary jabs at Modern Portfolio Theory. One footnote for example explains that MPT’s aim of lowering variance, thus its habit of treating the left-hand tail and the right-hand tail as equally undesirable, is rational only if there is certainty about the future mean return, or if “the investor can only invest in variables having a symmetric probability distribution.” And the authors consider neither premise plausible. The latter they find especially “farfetched.” From MDH to Entropy To get a bit more technical, their discussion elaborates on an existing literature on the “mixture” of two or more normals, the “mixture of distributions hypothesis.” It has been part of the finance literature for at least twenty years, since Matthew Richardson and Thomas Smith wrote a paper of the “daily flow of information” for the Journal of Financial and Quantitative Analysis in 1994. The underlying idea of the MDH is that information is moving into markets at uneven rates, and that this unevenness renders asymmetric distribution curves inevitable. In 2002, Damiano Brigo and Fabio Mercurio used MDH to calibrate the skew in equity options. What Geman et al. add is a model that makes “estimates and predictions under the most unpredictable circumstances consistent with the constraints.” They also, somewhat confusingly, call this a “maximum entropy” model. Entropy of course is a concept taken from the physical sciences, and the maximum entropic state for any system is one in which all useful energy has been converted into heat. Not a good thing. The idea has long been adopted into information theory, re-conceiving useful energy as signal and heat as noise. Thus, unsurprisingly, early efforts to introduce entropy into finance have seen entropy as something to be minimized. The Question in Unanswered Indeed, Geman et al are aware that their invocation of “maximum entropy” will seem an odd innovation to many of their readers. Most papers that have invoked entropy “in the mathematical finance literature have used minimization … as an optimization criterion” they say. Their use of a “maximum entropy” model (not as a “utility criterion” of course but as a way of recognizing “the uncertainty of asset distributions”) is itself not entirely novel though. They seem to have imported it from the world of developmental economics. In 2002 Channing Arndt, of the UN’s World Institute for Development Economics Research, witrh two associates, published an article announcing a ” maximum entropy approach” to modeling general equilibrium in developing economies, illustrating it with specific reference to Mozambique. Geman at al deserve some credit for their syncretism, their willingness to look in a variety of different places for the solution to the puzzle they’ve set themselves. Still, it seems to this layperson expert-on-none-of-it that the resulting construction is a ramshackle hut rather than a model. The simple question of why barbells work remains (so far as I can tell) unanswered.