With behavioral finance now as a way to explain momentum logically, momentum research took a giant leap forward with the publication of “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency” by Jegadeesh and Titman (1993). (Location 523)
According to rules-based momentum, you buy the strongest 10% to 30% stocks over the past 6 to 12 months, hold them 1 to 3 months, then reevaluate and rebalance the portfolio. (Location 533)
AQR’s funds select the top one-third of stocks using momentum measured over a 12-month look-back period, excluding the last month. (Location 569)
All of these publicly available products apply relative strength momentum to individual stocks. (Location 574)
Markowitz had taken an idea out of the realm of operations research (quadratic programming) to create an optimization algorithm that he used to map out the “frontier” of these efficient portfolios. (Location 598)
During the oral exam defending his Ph.D. thesis, Markowitz was challenged by Milton Friedman for over an hour. Friedman argued that Markowitz’s research was not economics, business administration, or even mathematics. (Location 601)
As is common with many economic models, the assumptions underlying MVO do not fit the real world very well.2 MVO results are unstable when the covariance (the combination of correlation and volatility) matrix is ill conditioned, which is brought about by having very similar assets. (Location 604)
What early CAPM did was a linear regression between the excess return (return less the risk-free rate) of an asset (or portfolio of assets) and the excess return of the market index. (Location 620)
The beta coefficient of the CAPM regression equation tells you the sensitivity of an asset’s excess return to variations in the market’s excess return. (Location 622)
The intercept of the regression equation is the alpha. It is what you have left over once you remove beta from the equation. Alpha represents abnormal profits. It is what you earn in excess of the reward for assuming market risk. (Location 625)
They say there are two ways to gain a positive expected return. The first is by taking on known risk factors (beta). The second is by outsmarting everyone else (alpha). (Location 633)
Returns of high beta portfolios were too low, and returns from low beta portfolios were too high. Much of the variation in expected return was unrelated to a portfolio’s beta. (Location 635)
Fischer Black said, “In the end, a theory is accepted not because it is confirmed by conventional empirical tests, but because researchers persuade one another that the theory is correct and relevant.” (Location 642)
Future returns on low P/E, low book-to-market, and small-cap stocks appeared to be higher than predicted by their betas. This resulted in the seminal Fama and French (1992) study that expanded CAPM from a single factor to a three-factor model by adding value and size risk factors to the single market factor. (Location 644)
Academics then became factor happy. At least 82 of them have been published in leading academic journals. Searching diligently for explanatory factors is reminiscent of Procrustes, the Greek mythological figure who made his visitors fit his bed by either stretching them or cutting off their legs. (Location 649)
Financial models generally rely on two main assumptions. The first is that market prices adhere to a normal or lognormal distribution.8 The second assumption of modern finance is that prices are independent of one another. Yesterday’s prices should have no influence on today’s prices. (Location 660)
Mandelbrot (2004) courageously challenged both these assumptions. He demonstrated that market prices do not follow a normal distribution, but rather one associated with unstable variance and fatter tails denoting a higher frequency of extreme events. (Location 662)
Unfortunately, many academics stopped paying attention to Mandelbrot’s financial concepts once they realized that these concepts challenged the usual assumption suspects, which had become part and parcel of most financial models. Mandelbrot’s unbounded, nonfinite variance calculations were also difficult to work with.9 (Location 670)
Warren Buffett, who rivals Yogi Berra as originator of some of the world’s best quotes, once said, “Beware of geeks bearing formulas.” In terms of developing models that can accurately explain and deal with the real world, financial economists have not been very successful. (Location 677)
Bachelier reasoned that if an option was a “fair bet,” then it had to have a fair value. (Location 686)
Since the time of Irving Fisher, economists have been enamored with the tidiness of equilibrium-based models. Inspired by Bachelier’s random walk ideas, Thorp and Kassouf (1967) came up with an equilibrium-based options model, but their published work did not discount back the expected value of an option at expiration. (Location 688)
Institutional investors were slow to learn their lessons. Following the collapse of LTCM in 1998, Merrill Lynch warned that mathematical risk models “may provide a greater sense of security than warranted; therefore, reliance on these models should be limited.”10 No one listened to them at that time. (Location 700)
Another hailed innovation of modern finance was portfolio insurance. This concept was developed by several finance professors who said investors should increase their long exposure when markets move up quickly and decrease their long exposure when markets drop quickly. (Location 720)
The public often reacts incorrectly by selling into weakness and buying into strength. (Location 723)
Portfolio insurance helped turn a market correction into a full-scale panic. Portfolio insurers packed up their bags following this market collapse and subsequent rebound (“mean reversion happens”) that gave them large whipsaw losses. (Location 729)
Belief in self-adjusting, rational markets may bear some responsibility for the global financial crisis of 2007–2008. (Location 732)
Perhaps Volcker made some sense when after the last financial crisis he said that the only worthwhile financial innovation of the past 20 years was the automated teller machine. (Location 735)
Getting rid of diversifiable risk this way is as close as one can get to a free lunch with respect to investing. (Location 740)
The second important finding of modern finance was greater awareness of the high price that investors pay for professional investment management. (Location 743)
IF YOU ASK MOST ACADEMICS about the effectiveness of momentum, they will likely say that it works very well. If you ask them why it works so well, you may get a blank stare. (Location 758)
Next, knowing how and why momentum works could give some insight into how the markets in general function. This may be useful in helping us understand the psychological biases that affect investor behavior in general, as well as our own behavior and motivations as investors. (Location 762)
Finally, understanding how momentum works may give us a better understanding of whether momentum profits are likely to remain strong in the future. (Location 765)
One of the first attempts at a risk-based explanation for momentum was by Conrad and Kaul (1998). They postulated that cross-sectional variations in expected returns of individual stocks could account for momentum profits. (Location 781)
Countering the use of additional risk factors, Griffin, Ji, and Martin (2003) showed that macroeconomic risk variables do not explain momentum profits. (Location 792)
Fama (1998) suggested that behavioral biases could be subject to “model dredging,” where one tries to find biases to fit the facts. (Location 796)
Instead, as we just saw, it was the supporters of efficient markets who kept trying to find additional risk factors they hoped would explain momentum on a rational basis. (Location 799)
Social psychology has always had a strong connection with stock market investing. (Location 803)
According to Graham and Dodd (1951), “The prices of common stocks are not carefully thought out computations, but the resultants of a welter of human reactions.” (Location 805)
Prospect theory showed that people value gains differently than they value losses. Investors being more sensitive to losses than they are to gains became known as “loss aversion.” Prospect theory helped explain why individuals make decisions that can deviate from rational decision making. (Location 809)
Anchoring is the tendency to overweight the importance of the first information that we learn. (Location 822)
According to Meub and Proeger (2014), anchoring can occur on a social dimension as well as on the individual level. Social anchoring can increase pressure toward conformity and acceptance of the status quo. (Location 824)
Confirmation bias is perhaps the oldest known behavioral heuristic. (Location 829)
Investors subject to confirmation bias who look at recent price moves as representative of the future may invest more in securities that have recently done well and less in those that have not done as well. (Location 837)
Bikhchandani, Hirshleifer, and Welch (1992) described informational cascades that cause traders to jump on the bandwagon where the herding effect feeds upon itself. (Location 845)
Charles MacKay wrote in his classic book of 1841, Extraordinary Popular Delusions and the Madness of Crowds, “Men, it has been well said, think in herds; it will be seen they go mad in herds, while they only recover their senses slowly, and one by one.” (Location 850)
Herding has a strong physiological, as well as psychological, basis. It is associated with the release of oxytocin and positive feelings of trust and security. (Location 852)
Herding is primordial. It manifests itself when an animal stays with the crowd in order to reduce its risk of attack. Herding is deeply ingrained in our brain chemistry and DNA. (Location 854)
Kandasamy et al. (2014) showed that investors experience a sustained increase in the stress hormone cortisol when market volatility increases, which causes them to become more risk-averse. (Location 857)
We will see in later chapters how absolute and dual momentum can help by removing falling assets from our portfolios before our stress levels become high enough to cause us to behave in ways that are injurious to our financial well-being. (Location 860)
There are several other theories advanced as to why investors follow positive-feedback strategies that lead to herding behavior. (Location 862)
Daniel, Hirshleifer, and Subrahmanyam (1998) propose a feedback model that incorporates investor overconfidence and biased self-attribution. (Location 866)
Kahneman (2011) said, “We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events.” Overconfidence can lead to suboptimal outcomes. It is often the strongest swimmers who drown. (Location 868)
The Barberis et al. (1998) and Daniel et al. (1998) explanations of momentum profits are based on market inefficiencies due to investor behavior. (Location 873)
The disposition effect, coined by Shefrin and Statman (1985) and confirmed by Grinblatt and Han (2005), is the tendency of investors to sell their winners too early in order to lock in gains, while holding on to losers too long in the hope of making back what they have lost. (Location 882)
Frazzini (2006) showed that the disposition effect leads to underreaction to news events among mutual fund managers. (Location 886)
Odean (1998), looking at the trading records of 10,000 individual investors in the 1980s, found that investors sell stocks for a gain 50% more frequently than they sell stocks for a loss. He found that the disposition effect costs investors, on average, 4.4% in annual returns. (Location 890)
The disposition effect impedes an asset’s rise to true value due to premature selling and to buying inertia. Anchoring and confirmation bias can also keep prices from reflecting their true values. (Location 894)
Thus, herding/anchoring/confirmation bias and the disposition effect complement each other and can lead to a unified, behaviorally based concept of momentum-inducing behavior. (Location 896)
WE ALL WANT AN INVESTMENT that will capture the highest possible risk premium while minimizing tail risk or drawdown. (Location 913)
In the preface to his classic book, Stocks for the Long Run, Jeremy Siegel (2014) writes, “over long periods of time, the returns on equities not only surpassed those on all other financial assets, but were far safer and more predictable than bond returns when inflation was taken into account.” (Location 914)
The average annualized real return after inflation on U.S. long-term government bonds from 1900 through 2013 was just 1.9%, considerably less than the 6.5% average annualized real return from U.S. equities during this same period. (Location 920)
Historically, investors have used bonds to diversify their stock portfolios and to reduce portfolio volatility. (Location 928)
During the 2007–2008 financial crises, bonds held up relatively well with respect to stocks. However, that has not always been the case. Stocks and bonds have been positively correlated nearly 70% of the time since 1973. (Location 929)
You can see that the correlation between stocks and government bonds has been greater than zero more than half the time.3 (Location 933)
Bonds can also be less stable than stocks and just as vulnerable to extreme losses. (Location 936)
When looking at 10-year holding periods, the worst stock performance was actually better than the worst bond performance!4 (Location 939)
Only about half of U.S. households hold equities, including what they have as retirement assets. (Location 947)
In their paper “Myopic Loss Aversion and the Equity Premium Puzzle,” Bernartzi and Thaler (1995) make the case that investors do not hold more stocks relative to bonds because investors focus too much on short-term performance and volatility instead of long-term performance goals. (Location 948)
As we will see in later chapters, the risk-reducing nature of absolute and dual momentum can help make this goal a reality. (Location 952)
Here is what Warren Buffett wrote about fixed-income investing in his 2012 annual letter to Berkshire Hathaway, Inc., shareholders: They are among the most dangerous of assets. Over the past century these instruments have destroyed the purchasing power of investors in many countries, even as these holders continued to receive timely payments of interest and principal … . Right now, bonds should come with a warning label. (Location 960)
The question then is should one ever have a permanent allocation to bonds when absolute (and dual) momentum can reduce the downside exposure of a stock portfolio? Absolute momentum uses bonds, but only when stocks are weak and bonds are strong. (Location 964)
A dynamic asset allocation methodology like absolute momentum will utilize either stocks or bonds, but only at the most appropriate times. (Location 966)
Later I will show how conservative investors, such as those past or nearing retirement age or with a strong aversion to risk, can use a modest allocation to bonds in order to dampen the short-run volatility of a dual momentum portfolio. I will also show how to apply dual momentum to the bond market itself in order to enhance bond returns and reduce their downside exposure. Due to cognitive dissonance and anchoring bias, it may take the next serious bear market in bonds for investors to give up the notion that permanently holding a substantial amount of bonds in their long-term portfolios is the prudent thing to do. (Location 968)
There are a number of “risk parity” programs that hold more than 75% of their portfolios in bonds in order to equalize stock and bond volatility. (Location 974)
Risk in leveraged portfolios has many facets, such as kurtosis (fat tails), illiquidity, counterparty, and contagion risk. (Location 977)
leverage. Risk parity investors may just be exchanging equities-based risk for other forms of risk that can be equally as problematic. (Location 979)
The largest risk parity program, Bridgewater’s $79 billion All Weather Fund, had a loss of 8.4% on $56 billion of inflation-linked debt, forcing it to reevaluate its heavy reliance on fixed income. (Location 981)
We utilize bonds when they are in the best position to add value to our portfolio instead of being a likely drag on portfolio performance. (Location 983)
Diversification is an age-old concept. It showed up in the Babylonian Talmud 1,500 years ago: “A man should always place his money one-third in land, a third in merchandise, and keep a third ready to hand.” Ecclesiastes 11:2 tells us to “divide your portion to seven or even eight, for you do not know what misfortune may occur on earth.” In The Merchant of Venice, Shakespeare wrote, “My ventures are not in one bottom trusted, nor to one place; nor is my whole estate upon the fortune of the present year. Therefore, my merchandise makes me not sad.” (Location 985)
A popular saying is that diversification works well … until it does not. Correlations tend to rise sharply during periods of market stress, which is when diversification is needed the most. (Location 991)
Long-run returns of U.S. stocks have been substantially better than the returns of non-U.S. stocks. Correlations between U.S and non-U.S. stocks have risen in recent years. (Location 997)
However, given the way different markets come in and out of favor, non-U.S. stocks may still add value to a portfolio based on relative strength momentum. (Location 1001)
Because emerging markets can suffer sharp and rapid price declines, you often see them aggregated into baskets that trade as a group. (Location 1006)
Aggregation and contagion can also amplify liquidity risk. During the Russian debt crisis, emerging markets as far away as Singapore suffered major capital outflows and extreme price volatility. (Location 1009)
Another volatile asset class that has attracted a considerable following in recent years is commodity futures. (Location 1019)
Yet common stocks over the long run, Treasury Inflation-Protected Securities (TIPS), and even Treasury bills can also serve as a hedge against inflation. (Location 1021)
An asset class is a portfolio of homogeneous assets delivering a positive excess return above the risk-free rate in the long run, corresponding to a “risk premium” or reward for the risk associated with holding that asset. (Location 1023)
In the1980s, when I was managing large commodity pools, buyers of commodity futures enjoyed a systematic positive return called the “roll yield” or “roll premium” that flowed from hedgers to speculators. Hedgers would pay what amounted to an insurance premium to speculators in order to shed themselves of the risks that they were unwilling or unable to bear. (Location 1036)
These new speculators tend to go long regardless of price. As the number of insurance providers (speculators) became increasingly large compared to the number of insurance buyers (hedgers), the roll yield dissipated and is now negative. From 1969 to 1992, the roll return averaged 11% per year. Since 2001, it has averaged a negative 6.6%.12 (Location 1053)
Furthermore, during both the 1929 stock market crash and the 2008 financial crisis, the correlation between equities and commodities shot up to over 80%. (Location 1069)
Much of what I said about passive commodities is also applicable to actively managed commodity futures. Managed futures typically use trend-following methods to participate on both the long and the short side of the commodity futures markets. (Location 1088)
As with other hedge funds, CTA fees are often 2% of assets and 20% of profits each year. During this period, aggregate CTA fees averaged 4.3% per year, which was more than twice what investors received. (Location 1110)
In 1952, Jones opened the fund to new investors and altered the structure by converting it from a general partnership to a limited partnership. (Location 1133)
The hedge fund industry did not really get off the ground until 1966, when an article in Fortune magazine highlighted how Jones’ obscure private investment fund had outperformed every mutual fund by high double digits over the prior five years. (Location 1137)
In an effort to maximize returns (and performance fees), many funds turned away from Jones’ long/short hedged strategy but retained the leverage feature. (Location 1140)
Managed futures are but one category of hedge fund. There are 18 others, the majority of which are long only. Drawn like moths to a flame by their strong desires to enhance performance and diversify more broadly, institutions have greatly stepped up their hedge fund investments. (Location 1151)
Private equity encompasses several long-term illiquid investment strategies. It began as a rebranding of leveraged buyouts after the 1980s. Private equity also includes venture capital, private growth capital, and distressed capital/special situations. The amount of private equity assets under management in 2012 was around $2 trillion, comparable to the amount in hedge funds. (Location 1187)
Buyout funds have been much riskier than the S&P 500 and have historically had significantly higher excess returns. (Location 1189)
Over the past 40 years, venture capital funds have had annual gains of 13.4%, versus 12.4% for the S&P 500 Index and 14.4% for the S&P SmallCap Growth Index. Venture capital funds have had higher volatility, illiquidity, and survivorship risk. (Location 1193)
According to Harris, Jenkinson, and Kaplan (2013), since 2000 the average venture capital fund has underperformed public markets by about 5% over the life of the fund. (Location 1195)
Buffett puts his own money where his mouth is. Buffett’s Berkshire Hathaway stock will go to charity after his death. For his heirs, Buffett has instructed the trustees of his estate to place 10% of what remains into short-term government bonds and 90% into a low-cost S&P 500 Index fund. (Location 1240)
The fees for active management came to 100% of the incremental returns earned over widely available passive alternatives. (Location 1250)
Whether through mutual funds or managed accounts, there is, in aggregate, little or no reward for bearing the additional costs of active management. (Location 1253)
If active investment management and actively managed mutual funds offer no advantage over passive index funds, then how have individual investors done on their own? (Location 1259)
We already know that much of this underperformance is due to investors making poor timing decisions due to their emotional responses to the markets. Investors sell after extended losses and are out when the market rises. Let us look now at some other studies so we can understand more about individual investor behavior. (Location 1266)
Studies show that momentum works well with almost any asset class.23 However, we can maximize our return through intelligent asset choice. (Location 1293)
Bonds, with a real return of 3.8%, are like a gentle breeze. Non-U.S. equities, with a risk premium somewhere between these two, are a steady zephyr. (Location 1296)
WE HAVE SEEN WHY WE want to focus on stocks and, to a lesser extent, bonds in our application of dual momentum. (Location 1306)
Smart beta is a catchall term for rules-based strategies that do not use conventional stock market index capitalization weights. (Location 1309)
The global financial crisis of 2007−2008 that led to more interest in diversification and risk control was also an impetus toward smart beta strategies. (Location 1316)
Interest in smart beta has recently skyrocketed. According to State Street Advisors, smart beta ETFs attracted $46 billion in 2013 and over $80 billion during the preceding three years. (Location 1317)
The first problem with smart beta is that, like unicorns, there is no such thing. Since beta is simply a portfolio’s sensitivity to movements in the overall market, it cannot be smart or dumb, although those who use it certainly can be. (Location 1323)
The expression smart beta makes as much sense as smart correlation, smart standard deviation, or smart Justin Bieber. Morningstar has now sensibly renamed smart beta as “strategic beta.”1 (Location 1325)
Chow et al. (2011) show that any apparent outperformance of alternative beta strategies over capitalization-based indexes is due to their exposure to value and small-cap factors. (Location 1330)
Smart beta investors in PRF are paying an additional fee for the construction of an index that is very similar to a traditional mid-cap value index. (Location 1336)
Back in the early 1970s, some of the early adopters of passive investing chose equal-weighted portfolios but gave up on that idea because of the issues of higher turnover, higher volatility, and having to invest large amounts in illiquid stocks. (Location 1357)
William Sharpe and Eugene Fama have called smart beta (or fundamental indexing) a marketing ploy.4 George “Gus” Sauter, former chief investment officer at Vanguard Group, said, “These so-called smart betas are not by definition adding alpha; they’re merely delivering factor exposures in more costly ways.” (Location 1384)
However, when looking at smart beta strategies, one needs to keep in mind that some of them have only around 15 years of backtest history and are still unproven. (Location 1393)
I use the following criteria when I construct dual momentum portfolios to determine if a nontraditional smart beta strategy might be worth considering as a substitute for a capitalization-weighted index and is more than just a high-cost, factor-based closet index fund: (Location 1400)
1 Does the approach make logical sense? Are there concepts underlying the strategy that have proven themselves? How likely is it that the strategy will continue to give better than market risk-adjusted returns? 2 Does the approach hold up under rigorous backtesting? Does it show robustness by being consistent across multiple markets and/or different periods? 3 Anomalies often show a decrease in profitability over time due to increases in trading activity.6 Are strategy transaction costs and fund expense ratios low enough so the approach can hold up to possible declining gross profits? 4 Is volatility of the strategy within a reasonable range? The marketplace may not compensate high volatility. High volatility may also contribute to greater tracking error. (Location 1403)
5 Is there decent liquidity in whatever investment vehicles are available for this strategy? (Location 1412)
A number of researchers have shown that the small-size premium has largely disappeared since at least the 1980s. (Location 1421)
Small-size stocks have more liquidity than they used to, however, because of the proliferation of funds that jumped on the small-cap bandwagon. (Location 1425)
Increased participation in small caps may explain why the small-size premium has become statistically insignificant since the early 1980s. (Location 1429)
There are now hundreds, if not thousands, of investment programs and funds incorporating a tilt toward value stocks. (Location 1440)
The 1992 and 1993 Fama and French studies that started all the excitement about value investing and were the impetus for so many value-oriented funds and portfolios covered a similar 1963–1991 period. (Location 1449)
Value may or may not be a robust driver of abnormal returns, but there is little doubt that momentum is the king of all market anomalies. (Location 1461)
Israel and Moskowitz (2013) also included cross-sectional stock momentum in their analysis using a 12-month look-back period while skipping the last month. (Location 1464)
The momentum effect is also positive and statistically significant in every 20-year subperiod. There has been no diminution in its effect during the most current 20-year period. Reliable alphas have ranged from 8.9% to 10.3% per year over the four subperiods before transaction costs. (Location 1466)
Investors making poor timing decisions accounted for much of the remaining 6% of annual underperformance. (Location 1478)
There is a strong propensity for investors to buy near market highs and sell near market bottoms due to what John Maynard Keynes called “animal spirits.” Others have called it fear and greed. The greater the volatility, the more pronounced the effect. (Location 1482)
We should have a good understanding of relative strength momentum from what we learned in Chapter 2. We saw how relative strength momentum gives investors a disciplined framework to work with. (Location 1492)
This can be a problem when using relative momentum, since relative strength does little to reduce downside exposure. Relative momentum may even increase downside volatility. Absolute momentum can help overcome this obstacle, so it is important that we understand it well before moving forward. (Location 1494)
Relative strength compares an asset to its peers in order to predict future performance. In academic research, relative momentum is often the same as cross-sectional momentum, which involves sectioning a universe of individual assets into equal segments and comparing the performance of the strongest segments (“winners”) to the performance of the weakest (“losers”). (Location 1497)
Momentum, however, also works well on an absolute or longitudinal basis, in which an asset’s own past predicts its future. (Location 1501)
I prefer to call what we have here absolute momentum because practitioners are used to hearing about relative and absolute returns. They measure relative returns against other assets or a benchmark, while absolute returns are those returns with respect to just an asset itself. (Location 1504)
In absolute momentum, we look at an asset’s excess return (its return less the return on Treasury bills) over a given look-back period. (Location 1507)
Absolute momentum is roughly the same as relative momentum applied to an asset paired up with Treasury bills. (Location 1509)
In simpler terms, absolute momentum asks if an asset has been going up or going down over the look-back period. (Location 1509)
Absolute momentum is a bet on the continuing serial correlation of returns, or, in cowboy terms, absolute momentum says, “A horse is easiest to ride in the direction it’s already going.” (Location 1511)
Absolute momentum is quintessential trend following.3 (Location 1519)
Some well-known discretionary momentum investors of the past, such as Gerald Tsai, quickly went from hero to zero when they were unable to determine a change in market direction. (Location 1521)
Trend-following methods, in general, have slowly achieved some recognition and acceptance in the academic community. (Location 1524)
A recent paper by Lemperiere et al. (2014) called “Two Centuries of Trend Following,” identified highly significant anomalous excess returns based on an exponentially weighted moving average strategy applied to four asset classes (stock indexes, commodities, currencies, and bonds) across seven countries. Results were stable across both time and asset class, extending all the way back to 1800 for stock indexes and commodities. (Location 1527)
Relative strength momentum looks at the trend of one asset compared to another, while absolute momentum looks at the trend of an asset with respect to its own past. (Location 1530)
You can apply absolute momentum to a single asset, whereas you need two or more assets to use relative momentum. (Location 1534)
Absolute momentum lets you hold on to all your assets, as long as their trends remain positive. (Location 1536)
Absolute momentum therefore gives greater diversification than relative momentum, which can in turn lower a portfolio’s short-run volatility. (Location 1536)
Absolute momentum helps one to follow the maxim of the great trader Paul Tudor Jones: “The most important rule in trading is: play great defense, not great offense.” (Location 1539)
Shorter-term bonds are usually stronger than stocks when stocks are trending down. Selecting bonds over stocks during such times is a way of using absolute momentum. (Location 1542)
The authors found that a 12-month look-back period had the highest statistical significance from among a range of 1 to 48 months when used with a one-month holding period. (Location 1545)
There was little correlation with passive benchmarks in each asset class or to the standard asset pricing factors. Returns were largest when stock market returns were the most extreme, which means absolute momentum can function as a hedge to extreme events. (Location 1547)
In their research, absolute momentum performed well in extreme market environments and across 59 markets covering four asset classes (commodities, equity indexes, bond markets, and currency pairs). The authors showed that absolute momentum has been consistently profitable all the way back to the year 1903. (Location 1551)
In that paper, I demonstrated how absolute momentum gave better long-run results than relative momentum. Not only did absolute momentum offer higher expected returns but, unlike relative momentum, it also substantially reduced downside exposure during bear markets. (Location 1558)
I demonstrated how absolute momentum can improve risk parity portfolios by reducing their exposure to bonds and their need for leverage. (Location 1562)
Even though absolute momentum often gives better risk-adjusted results than relative momentum, most practitioners use only relative momentum. Absolute momentum is still relatively unknown and has not yet attracted much attention. (Location 1569)
The best approach is to use absolute and relative together in order to gain the advantages of both. The way we do that is by first using relative momentum to select the best-performing asset over the preceding 12 months. We then apply absolute momentum as a trend-following filter by seeing if the excess return of our selected asset has been positive or negative over the preceding year. If it has been positive, that means its trend is up and we proceed to use that asset. If our asset’s excess return over the past year has been negative, then its trend is down and we invest instead in short- to intermediate-term fixed-income instruments until the trend turns positive. (Location 1571)
There are some potential problems, however, with the statistical properties of the Sharpe ratio. For example, rankings based on Sharpe ratios can be misleading if they are not adjusted for the impact of serial correlation. (Location 1588)
The difference between upside and downside volatility can be particularly problematic when returns are highly skewed, or nonsymmetric. Stock market returns are often negatively skewed, with an asymmetric left tail extending more toward negative values.12 This creates tail risk, which can lead to greater-than-expected losses and aggravate the animal spirits mentioned earlier. Positive skewness is much preferred, since surprises should then work in our favor. (Location 1599)
Another simple indicator of tail risk that is intuitive, easy to understand, and relatively easy to calculate is maximum drawdown.14 Drawdown is the percentage that price moves down from a new high. Since we use monthly returns, maximum drawdown to us means the maximum cumulative peak-to-valley retracement on a month-end basis. (Location 1609)
Based on the past success and high-risk premium of equities, we will anchor our investment portfolio in U.S. stocks and switch into non-U.S. stocks in accordance with relative strength momentum. (Location 1630)
We will hold bonds (short to intermediate term) only when the U.S. and non-U.S. equity markets are not in an uptrend, as determined by absolute momentum. (Location 1631)
Our dual momentum approach using both absolute and relative momentum to manage asset allocation is a major paradigm shift from what investors usually have done. (Location 1635)
For example, when U.S. and non-U.S. equities enjoy returns that are one standard deviation above average, which is typical of bull markets, their monthly correlation is –0.17. However, when equity returns are one standard deviation below average, which is typical of bear markets, the monthly correlation between U.S and non-U.S. equities rises to 0.76. (Location 1639)
The momentum formation or look-back period is the amount of history we use to measure momentum and select our momentum-based portfolio. We saw earlier that the best look-back period across most markets is generally 6 to 12 months. (Location 1644)
A number of commercial momentum applications also use a 12-month look-back period.1 We will similarly use a 12-month look-back period and apply it to both types of momentum. Other look-back periods also deliver satisfactory results. However, a look-back period at the long end of the 6- to 12-month effective range minimizes portfolio turnover and transaction costs. (Location 1647)
In dealing with individual stocks, one often skips the most recent week or month in order to disentangle the intermediate momentum effect from the (Location 1650)
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short-term reversal or contrarian effect in returns at the one-week or one-month level. This is said to be related to liquidity or microstructure issues. We will use broad-based stock market indexes for our momentum model, because they are less subject to noise than individual stocks, and their transaction costs are much lower. Indexes are also less subject to liquidity and microstructure issues, so we will not need to skip a month. (Location 1651)
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We will first look at absolute momentum applied to the S&P 500 Index. This means if the S&P 500 shows a positive excess return (return less the Treasury bill return) during the past 12 months, we stay invested in it. If the prior 12-month excess return of the S&P 500 is negative, we exit the S&P 500 Index and hold the Barclays U.S. Aggregate Bond Index instead. (Location 1655)
What we are doing in effect is staying in stocks if the market has been up for the past year and exiting to the safety of shorter-term bonds if the stock market has been down for the past year. Our approach is both simple and easy. (Location 1661)
Something as simple as the application of 12-month absolute momentum gives impressive results. (Location 1670)
Average annual return increases by more than 200 basis points over the S&P 500 Index by itself, while the annual standard deviation drops by more than 3%. Maximum drawdown goes from over 50% to less than 30%. (Location 1671)
Being a long-term trend-following approach, absolute momentum will not respond to short-term market corrections such as the one that occurred in October 1987. (Location 1674)
However, absolute momentum gave back relatively little in accumulated profits before exiting stocks. It would have taken us out of harm’s way early during each bear market. (Location 1680)
However, unlike an actual stop loss, absolute momentum has an inherent way to reenter the market once the trend turns positive again. (Location 1682)
Each month we can apply absolute momentum to ACWI by switching between it and the Barclays U.S. Aggregate Bond Index based on whether the excess return of the S&P 500 has been positive or negative during the past 12 months. (Location 1700)
Absolute momentum is particularly helpful in bear market environments, such in 2000–2002 and 2007–2008. (Location 1712)
Relative momentum adds more return than absolute momentum, but it does so with considerably more volatility and drawdown. (Location 1717)
Investors currently use relative momentum much more than they use absolute momentum. Absolute momentum, though, with its substantial decrease in volatility and drawdown and its higher Sharpe ratio, is superior on a risk-adjusted basis. (Location 1721)
We can benefit from the complementary nature of relative and absolute momentum by using them both together. This is what dual momentum is all about. (Location 1723)
Aggregate bonds will again serve as a safe harbor during bear markets in accordance with our absolute momentum signals taken from the S&P 500. We will also switch between the S&P 500 and the ACWI ex-U.S. based on relative strength momentum. My name for this particular application of dual momentum is Global Equities Momentum (GEM). (Location 1726)
We first compare the S&P 500 to the ACWI ex-U.S. over the past year and select whichever one has performed better. We then check to see if our selected index has done better than U.S. Treasury bills. If it has, we invest in that index. If it has not, we invest instead in U.S. aggregate bonds. We repeat this procedure every month. (Location 1731)
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As you can see in Figure 8.5, the absolute momentum component in GEM kept us relatively safe from bear market erosions of capital. (Location 1742)
This contrary performance during bear markets shows that GEM could have a valuable place as a stabilizing and diversifying asset for those equity investors who do not want to use GEM as a core holding. (Location 1768)
To get a better sense of how and when GEM achieves its outperformance, Table 8.6 shows the number of years that GEM outperformed and underperformed the S&P 500 during up and down years for the S&P 500. Table 8.7 shows average annual returns in those up and down market environments. (Location 1772)
GEM investors still need to exercise patience during bull markets when GEM may just as often underperform as outperform market benchmarks. Investors need to remember that much of the outperformance of GEM occurs in bear market environments. (Location 1779)
Since the GEM model is in bonds around 30% of the time, we also show a five-factor model that adds the excess return of the Barclays Aggregate Bond index as an additional factor. (Location 1807)
GEM is not only effective in providing high and significant risk-adjusted returns, but it is also parsimonious. This is a word used by economists to make them look smart. It means simple and straightforward. (Location 1815)
Highly optimized methods are often complex, fragile, and prone to failure. GEM, on the other hand, is simple and robust. It uses only U.S. equities, non-U.S. equities, and aggregate bonds. (Location 1819)
You need to enter and save three ETF symbols, one each for U.S. stocks (SPY, IVV, or VOO), non-U.S. stocks (VEU or IXUS), and U.S. Treasury bills (BIL). (Location 1828)
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If one of the two equity ETFs shows the highest return over the past year, then that is your selection for the coming month. (Location 1829)
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Dual momentum usually sells losing positions, creating short-term capital losses while holding onto winning positions for long-term capital gains. Can there be any reason not to use GEM? (Location 1836)
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Dual momentum is simple and direct, based as it is on straightforward relative and absolute performance. Its main parameter is the look-back period. (Location 1864)
With so much going for dual momentum, if you try to replace or modify this proven approach with something new, you face several potential problems. First is the multiple-comparisons hazard that comes from data mining when it becomes data snooping. (Location 1870)
The same data-snooping problem exists after you choose a model and have to determine its parameters. (Location 1883)
There is also the problem of using the data twice, once to optimize a strategy and its parameters, and then again to judge how well the strategy predicts future returns. One needs to penalize complex or highly optimized models and to evaluate performance on fresh data not used for model development. (Location 1889)
Some practitioners use only around 15 years of data to design and backtest their trading models, since that is when a number of ETFs started trading. (Location 1897)
In contrast to this, significant and consistent risk-adjusted profits from absolute momentum go back to 1903. With relative momentum, results extend even farther back, to 1801. As far as I know, there is no other backtesting in financial markets that is based on this much data. (Location 1919)
There have been many attempts by practitioners over the years to engage in market timing and determine price trends. (Location 1927)
“Technical analysis is anathema to the academic world. We love to pick on it.” Just the words “market timing” would shut down most academic’s and many practitioner’s brain synapses. (Location 1929)
The authors found that among 95 modern studies of technical trading strategies from 1988 through 2004, there were 56 with positive results, 29 with negative results, and 19 with mixed results. (Location 1938)
We see that data mining for new trading rules is a perilous undertaking. Although bootstrapping can help establish confidence levels, especially when dealing with modest amounts of data, it may depend on assumptions about how the markets function that may not be realistic. (Location 1947)
Baltas and Kosowski (2012) came up with an alternative method for determining absolute momentum trends using a broad data set of 75 futures contracts from December 1975 through February 2013. They compared the usual method of determining absolute momentum that looks at the direction of preceding 12-month returns to an alternative method based on the t-statistic of the slope found by fitting a trend line to daily prices over the past 12 months. Their method reduced the transaction costs of absolute momentum by about two-thirds. (Location 1953)
Using data from 1897 through 1967, Gordon showed that buying stocks when the DJIA was above its 200-day moving average produced seven times the return as when the DJIA was below its 200-day moving average. (Location 1961)
Since 2002, Jeremy Siegel of Wharton has presented the 200-day moving average as a volatility-reducing filter in his popular book Stocks for the Long Run. Faber (2007) converted the 200-day moving average into an equivalent 10-month moving average, whereby one holds a long position when the price is above its 10-month moving average and exits the position when the price falls below the 10-month moving average. (Location 1964)
Results from the three strategies were very similar. Absolute momentum was in stocks 70% of the time. It had 31 trades over these 40 years, for an average of 0.83 trades per year. (Location 1979)
Absolute momentum therefore had lower transaction costs than the 10-month moving average. (Location 1981)
Moving averages and absolute momentum both try to identify trends by reducing noise. (Location 1982)
Some practitioners believe they can time the market by paying attention to valuation metrics, such as the Shiller 10-year Cyclically Adjusted Price Earnings (CAPE) ratio. (Location 1990)
Historically, CAPE ratios under 10 have led to future annual stock market returns of over 20%, while CAPE ratios over 20 have given future annual stock market returns of only 5%. (Location 1993)
The AQR Momentum Index is composed of the top one-third of the 1,000 highest capitalization U.S. stocks based on 12-month relative strength momentum with a one-month lag. AQR weights its index positions based on market capitalization. It adjusts its positions quarterly. (Location 2005)
AQR argue that momentum applied to individual stocks is worthwhile even if it were to show a zero return, provided one combines momentum stocks with value stocks. (Location 2020)
alone. However, there is little or no advantage with respect to maximum drawdown, and results still pale in comparison to absolute momentum combined with the Russell 1000 Index. (Location 2024)
In the 1950s, both Dreyfus and Darvas extolled the virtue of investing in stocks that were making new highs. In their 2004 paper “The 52-Week High and Momentum Investing,” George and Hwang showed that the 52-week high explains a large portion of the profits from momentum investing. (Location 2035)
The authors postulated that stocks near their 52-week high are those for which good news has recently arrived. If this is really the reason why being near a 52-week high is effective, then nearness to a 52-week high is likely to be more effective with individual stocks than with stock indexes or asset classes that may not be as sensitive to news events. (Location 2040)
In their 2013 paper, “Does Revenue Momentum Drive or Ride Earnings or Price Momentum?” Chen et al. (2014) examined the profitability of strategies based on price, earnings, and revenue momentum, both alone and in combination with one another. Looking at U.S. stocks from 1974 through 2007, the authors measured price momentum based on past stock returns, as is usually done. (Location 2043)
Using long/short hedged portfolios, the authors found that price momentum gave the largest average profit, followed by earnings, then revenue momentum. None of the three momentum strategies was dominant, which means that each carried some exclusive information content. (Location 2047)
Revenue and earnings momentum combined accounted for only 19% of the price momentum effects, so price momentum was the most important factor. (Location 2051)
Overall evidence suggests that a strategy combining past return, earnings, and revenue momentum outperforms strategies based on only one or two of these factors. (Location 2052)
When the authors sorted stocks based on this curvature, they found that the gross returns and three-factor alphas of convex (accelerating upward) positive momentum stocks were significantly higher than the gross returns and alphas of stocks with concave positive momentum. (Location 2059)
Docherty and Hurst (2014) took a similar approach in the Australian stock market using data from 1992 through 2011. (Location 2062)
In “Fresh Momentum,” Chen, Kadan, and Kose (2009) defined fresh winners as the strongest stocks during the previous 12 months that were relatively weak during the 12 months prior to that. (Location 2067)
One could easily apply this fresh momentum approach to stock indexes and other assets, in addition to individual stocks. (Location 2071)
The simplest extension of dual momentum is to start with the allocation given for conservative investors in Chapter 8 that permanently has 70% in GEM and 30% in U.S. aggregate bonds. This time, instead of holding the permanent fixed-income portion of the portfolio always in U.S. aggregate bonds, we will use dual momentum to select from among fixed-income alternatives. (Location 2074)
I call this dual momentum global stock/bond strategy my Global Balanced Momentum (GBM) model. GBM has 70% allocated to the same equities holdings as GEM, but its fixed-income holdings, including a permanent 30% allocation to fixed income, are selected using dual momentum. This means that the equity portion of the portfolio when stocks are weak, as well as the fixed-income portion of the portfolio can be in fixed-income alternatives, depending on which of them has been the strongest over the look-back period. (Location 2077)
We see that applying dual momentum to the fixed-income side boosts the annual return of the conservative 70% GEM/30% U.S. aggregate bond portfolio by over 150 basis points. (Location 2086)
They constructed an industry-based momentum strategy that produced the same average monthly returns as an individual stock momentum strategy. Momentum based on industries, or sectors of closely related industries, is much easier to implement than individual stock momentum, and transaction costs are considerably lower. (Location 2096)
The Morningstar sectors separate the U.S. stock market into 11 nonoverlapping segments. These cover technology, industrials, energy, communication services, real estate, financial services, consumer cyclical, basic materials, utilities, consumer defensive, and healthcare. (Location 2099)
We see clearly that absolute momentum offers both a higher return and a substantially lower drawdown than relative momentum. Dual momentum reflects the higher returns and drawdown reduction of applying absolute momentum to enter and exit the 11 equally weighted equity sectors, while also capturing the higher returns that sometimes come from relative strength momentum. (Location 2113)
These defensive sectors often hold up well following market tops and before trend-following absolute momentum can kick in and take us out of all equity positions. (Location 2116)
Warren Buffett also said, “Full-time professionals in other fields, let’s say dentists, bring a lot to the layman. But in aggregate, people get nothing for their money from professional money managers.” (Location 2131)
According to Lao Tzu, the best way to manage anything is by making use of its inherent nature. (Location 2139)
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Warren Buffett commented on this too, saying the only value of stock forecasters is to make fortunetellers look good. Sturgeon’s law also comes to mind: “Ninety percent of everything is crap.” Individual investors often overtrade, underdiversify, and succumb to a number of common behavioral errors. (Location 2144)
Under the old paradigm, investors who stand the best chance of succeeding are those using low-cost, passively managed index funds as recommended by Warren Buffett, Charles Schwab, John Bogle, Bernie Madoff (okay, forget Bernie Madoff), and others.1 Yet passive index funds are still subject to large drawdowns. They also may still trigger emotional responses and create unwise investor behavior at inopportune times. (Location 2155)
Due to myopic loss aversion and too much focus on short-term return variability, investors may hold more bonds than they should in order to maximize their long-run wealth. This aversion to equities has led to a higher equity risk premium. (Location 2161)
Furthermore, due to human inertia and lingering ignorance, it is unlikely that the majority of people will suddenly wake up and become enthusiastic momentum investors. This should help keep many investors trading against momentum rather than with it. (Location 2175)
People have known this for many years, yet over 70% of all funds are still actively managed.3 Similarly, ETFs have many advantages over mutual funds, such as intraday liquidity, lower expense ratios, and preferential tax treatment. Yet there is only $1.62 trillion invested in ETFs compared to $14.8 trillion invested in mutual funds. (Location 2178)
Momentum may very well show this same kind of disconnect. Some of those to whom I have explained dual momentum do not appreciate it as the “premier anomaly,” and regard it as a niche rather than as a core investment strategy. (Location 2181)
There will undoubtedly be periods when dual momentum underperforms its benchmarks. During those times, investors may lose sight of the big picture and be tempted to behave in ways that hurt them in the long run. (Location 2189)
Models beat experts 94% of the time. Human judgment prevailed over quantitative models in only eight studies, and all of these had access to information not available to the quantitative models. Even when given access to the quantitative model results, experts still underperformed the models. Quantitative models had become a floor rather than a ceiling. According to Grove et al., “Humans are susceptible to many errors in clinical judgment. These include ignoring base rates, assigning nonoptimal weights to cues, failure to take into account regression toward the mean, and failure to properly assess covariation.”5 (Location 2195)
Jim Simons, the billionaire founder of Renaissance Technologies and Medallion Fund, is one of the best systems traders on the planet. (Location 2200)
Simons says, “So if you’re going to trade using models, you should just slavishly use the models. You do whatever the hell it says no matter how smart or dumb you think it is now.” (Location 2201)
I have also come to recognize that I am no match for dual momentum and to value it as my best investment friend. (Location 2204)
Absolute momentum appears to be just as robust and universally applicable as relative momentum. (Location 2251)
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Despite an abundance of momentum research over the past 20 years, no one is sure why it works. (Location 2253)
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With relative strength momentum, on the other hand, we exclude or reduce the influence of some assets from the active portfolio. (Location 2267)
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The second advantage of absolute momentum is its superior ability to reduce downside volatility by identifying regime change. (Location 2269)
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A common way to construct risk parity portfolios is to weight each asset’s position size by the inverse of its volatility. (Location 2375)
Starting with the MSCI US and long Treasury bond indexes used in our 60/40 portfolio, we add REITs, credit bonds, and gold, with an equal weighting given to each asset class. (Location 2380)
REITs give us exposure to real assets with some additional risk exposure to equities. Gold gives us real asset exposure that is different from real estate.7 Gold has the highest volatility, and so it represents only 20% of our parity portfolio, whereas bonds receive the largest allocation of 40%, being represented twice in the portfolio. Exposure to equities is somewhere between gold and bonds. (Location 2383)