The Quantitative Momentum Investing Philosophy
The Quantitative Momentum Investing Philosophy
Buy Stocks with the Highest Quality Momentum
Our Quantitative Momentum (QM) system seeks to identify stocks with the highest quality “momentum.” We consider the term momentum to mean a continuation of past returns—past winners tend to be future winners, while past losers tend to be future losers. How can we exploit this phenomenon? At Alpha Architect, we have designed a system to identify the “quality” of momentum by examining how the momentum is formed.
But why might momentum be an interesting stock selection tool?
First, Eugene Fama, the 2014 co-recipient of the Nobel Prize in Economics and father of the efficient market hypothesis, has summarized the academic research on momentum as follows:
“The premier anomaly is momentum.”
When the father of efficient markets suggests momentum is the leading anomaly, we take note.
Second, the empirical research on the momentum effect is compelling. For example, academic researchers have examined stock data going back over 200 years and identified a significant and robust historical performance record. As natural skeptics, we have independently verified many of the empirical results associated with momentum. Momentum is well grounded, historically. And while we never want to invest in a strategy simply because it has a great backtest, we believe that the momentum anomaly is a sustainable active investment strategy. We believe the strategy can persist because the returns are 1) driven by innate human bias, and 2) following the strategy is difficult because of enhanced volatility and career risk considerations.
We minimize deeply ingrained human bias by following a systematic approach, which protects us from our own behavioral errors. Our tools do not necessarily need to be complex, but they do need to be systematic. We contend with volatility and career risk by educating investors on the long-term horizon required to be a successful momentum investor. We refuse to appease those with short horizons by “diluting” our Quantitative Momentum. Hence, our strategy is concentrated and follows an evidence-based methodology. In the end, we cannot guarantee long-term success, but our process does promise one thing: a high-conviction momentum strategy that is Built to Beat Behavioral Bias.
Quantitative Momentum (QM) has a straightforward mission:
- Identify the most effective way to systematically capture the momentum premium.
Our mission involves two core beliefs:
- Momentum investing works and is driven by a predictable underreaction to positive fundamentals.
- We can’t control our own biases, and therefore our decision-making process must be automated.
In 2012, Alpha Architect partnered with a multi-billion dollar family office and turned our dream to deliver affordable active management into a reality. At the time, we were focused on our Quantitative Value strategy. However, in the course of our extensive research and development efforts we created a momentum strategy that complemented our value strategy. In the end, we boiled down our momentum process into five sequential steps (depicted in Figure 1):
- Identify Investable Universe: Our universe generally consists of mid- to large-capitalization U.S. exchange-traded stocks.
- Generic Momentum Screen: We rank stocks within our universe based on their past twelve-month returns, ignoring the first month.
- Momentum Quality Screen: We screen high momentum stocks on the “quality” of their momentum—we focus on stocks with a “smoother” return path towards their high momentum status.
- Momentum Seasonality Screen: We take advantage of certain seasonal aspects applicable to momentum investing, which determines the timing of our rebalance.
- Invest with Conviction: We seek to invest in a concentrated portfolio of stocks with the highest quality momentum. This form of investing requires disciplined commitment, as well as a willingness to deviate from standard benchmarks.
Step 1: Identify the Investable Universe
The first step in the QM investing process involves setting boundaries on the universe for further screening. There are several reasons we place such limits around the stocks to consider. A critical aspect involves liquidity, which is related to the size of the stocks under consideration. In general, if we include stocks that are too small, the possibility of large price moves on small volume can lead to significantly overstated theoretical returns relative to actual returns. In other words, if we include small stocks in our universe, the back-tested results may generate phenomenal returns, but these returns may be unobtainable in the real world, even when operating with small amounts of capital.
In order to honestly assess and reliably implement the QM approach, we eliminate all stocks below the 40th percentile breakpoint of the NYSE by market capitalization. As of December 31, 2014, the 40th percentile corresponded to a market capitalization of approximately $1.9 billion. Our universe also excludes ADRs, REITS, ETFs, and firms without 12 months of return data.
In summary, our investment universe contains liquid companies with at least one year of return data.
Step 2: Generic Momentum Screen
In basketball, if a player has made a few shots in a row, the player is described as having a “hot hand;” in finance parlance, this player has “momentum.” But can basketball players actually exhibit momentum? Originally, the evidence seemed to reject such a theory, as outlined in a 1985 paper by Thomas Gilovich, Robert Vallone and Amos Tversky. For decades, the theory of a hot hand in sports was considered a myth. The question appeared settled. However, recent working papers by Andrew Bocskocsky, John Ezekowitz and Carolyn Stein in 2013, and Brett S. Green and Jeffrey Zwiebel in 2013, now show that the hot hand probably exists in basketball and also in baseball.
The intellectual journey to identify momentum in sports is similar to the attempts to identify momentum in stocks. Initially, stock momentum was deemed a myth because the efficient market hypothesis considered this approach to be impossible. Academics laughed at the idea. But contravening evidence began to mount…and mount…and mount. Today, no one is laughing. Serious evidence-based investors and academic researchers can no longer consider momentum heresy.
But how does one calculate momentum? When testing momentum in stock returns, we need to first identify the time period over which we will calculate the momentum variable. Below we summarize the main academic research findings for three different look-back momentum calculation periods:
- Short-Term Momentum (1-month) – exhibits a reversal in returns
- Long-Term Momentum (3 to 5 years) – exhibits a reversal in returns
- Intermediate-Term Momentum (6-12 months) – exhibits a continuation in returns
In short, both short-term and long-term momentum signal a future reversal in returns, in other words, one can expect these stocks to underperform. However, intermediate-term momentum provides a continuation of returns−the so-called “hot-hand”−and these stocks tend to outperform. We focus on this momentum measurement for Step 2.
In general, the momentum premium decays as the rebalance frequency decreases (e.g., monthly rebalanced portfolios beat annually rebalanced portfolios). However, costs increase with rebalance frequency. Thus, consistent with the law of diminishing returns, there is a point at which the momentum benefits of frequent rebalancing are overcome by costs. As a compromise, we examine a “sweet spot” 3-month rebalance for the portfolio analysis that employs overlapping portfolios. We only examine firms above the NYSE 40th percentile for market capitalization to eliminate smaller, illiquid firms. Portfolios are formed by equal-weighting the firms and the returns run from 1/1/1974 through 12/31/2015.
We sort stocks based on their cumulative 12 month past returns (ignoring the first month), and buy an equal-weighted basket of stocks from the top decile. We repeat this process each month. The returns shown below are net of 1.00% annual transaction costs (4 rebalances a year @ 0.25% per rebalance) and a 1.00% management fee. The SP500 index is gross of fees.
As shown in the table above, a generic momentum screen outperforms the passive market index based on annualized returns, Sharpe Ratio, and Sortino Ratio. However, a generic momentum portfolio also has more volatility and a larger drawdown. In the next step, we identify techniques to minimize the extra volatility and drawdowns associated with momentum strategies.
Step 3: Quality of Momentum Screen
Step 1 helps us identify a universe that is expected to be reasonably liquid, and Step 2 examines the results for our first screen−the generic momentum screen. In Step 3 we seek to identify the quality of momentum associated with the stocks from Step 2.
The details for calculating momentum quality are complex, but the intuition is simple. Consider two hypothetical momentum stocks: Stock A is a biotechnology company, Stock B is a Big Box Store, and both companies have a 200% return over the past 12 months. However, assume A and B have vastly different paths to 200 percent returns.
- Buzzing Biotech: Stock A’s returns were 0% for 11 months, but just recently Stock A was granted an FDA approval for a new drug and the stock shot up 200%.
- Boring BigBox: Stock B has returned 0.80% each day, on average, for the past 250 days, and has generated a 200% return.
Stock A and Stock B are both considered momentum stocks, but Buzzing Biotech’s path is much different from Boring BigBox’s path. So-called “path dependency” matters, if momentum is driven by an investor bias referred to as “limited attention.” For example, Buzzing Biotech’s FDA approval will likely be covered by the media and be highly available to investors, thus rapidly driving the company’s price to efficient levels. However, Boring BigBox is delivering news that is consistently better than market expectations, but over a longer period, and because the attention to Boring BigBox is limited, this good news is slow to be incorporated into market prices.
Although testing the “limited attention” hypothesis in the context of momentum is challenging, we’re lucky that finance professors have been hard at work. In a 2014 paper titled, “Frog-in-the-Pan: Continuous Information and Momentum,” Zhi Da, Umit Gurun, and Mith Awarachka find that high momentum firms with smooth, or “high-quality” momentum, tend to do better than those firms with choppy low-quality momentum. The results are summarized in Figure 3, which shows three-factor alpha estimates for long/short high-quality (“continuous”) and low-quality (“discrete”) momentum portfolios over various rebalance frequencies. 
Recall that the proverbial frog-in-the-pan sits in a pool of water whose temperature is gradually increasing. Because the change in temperature is so slow, the frog has limited attention to the rising heat and he slowly boils to death. Similarly, investors have limited attention to the ongoing flow of uneventful, but reliable information, arriving continuously in small amounts regarding a stock.
To calculate “frog-in-the-pan” momentum, the authors classify each daily return as either positive or negative (or zero in some cases). In general, a high-quality momentum stock should have a higher percentage of positive return days compared to a choppier stock. We conduct our own analysis of the frog-in-the-pan variable and incorporate this variable into our Quantitative Momentum system. In our context, we use the frog-in-the-pan measure to identify stocks from Step 2 that have high-quality momentum. We split the portfolio of high generic momentum stocks into high-quality momentum and low-quality momentum. The portfolio is equal-weighted. The returns shown below are net of 1.00% annual transaction costs (4 rebalances a year @ 0.25% a rebalance) and a 1.00% management fee. The SP500 index is gross of fees.
The results highlight that focusing on the high momentum stocks with quality momentum can improve CAGR, Sharpe and Sortino ratios.
Step 4: Seasonality Screen
Steps 1 through 3 focus on momentum stocks with quality momentum. Step 4 further enhances our Quantitative Momentum system by incorporating seasonality effects that have been documented in momentum strategy research. Some of the most compelling research on this subject is found in a 2007 paper titled, “Causes and Seasonality of Momentum Profits,” published in the Financial Analyst Journal by Rishard Sias. Professor Sias shows that window-dressing (i.e., when institutions buy stocks that have performed well so they can report ownership of “winning” stocks at quarter-ends) and tax incentives at year end drive momentum seasonality effects. Professor Sias summarizes his results:
“…the average monthly return to a momentum strategy for U.S. stocks was found to be 59 bps for non-quarter-ending months but 310 bps for quarter-ending months…investors using a momentum strategy should focus on quarter-ending months…”
Sias’s paper focuses on long/short momentum portfolios, but the conclusions regarding momentum seasonality can be incorporated into our long-only Quantitative Momentum system. We test our quality momentum strategy, but vary the start date of the portfolios. In Figure 5 below, we compare a quarterly rebalanced portfolio that incorporates seasonality effects by rebalancing near the beginning of quarter-ending months (column 1) to a portfolio to a portfolio that ignores seasonality (column 2). The returns shown below are net of 1.00% annual transaction costs (4 rebalances a year @ 0.25% a rebalance) and a 1.00% management fee. (There are competing arguments for transaction costs here and here).
As Figure 5 shows, forming the portfolio to exploit seasonality effects yields the highest CAGR, Sharpe and Sortino ratios.
Step 5: Invest with Conviction
Steps 1 through 4 systematically identify stocks with the highest quality momentum and take advantage of momentum seasonality. We believe we have identified a form of momentum investing that intelligently incorporates the best research on the subject into a coherent and pragmatic investment approach. But we can easily destroy the benefits of a reasonable investment process by mismanaging portfolio construction and “diworsifying” our active momentum exposure. Charlie Munger, at the 2004 Berkshire Hathaway Annual Meeting, is quoted as saying, “The idea of excessive diversification is madness…almost all good investments will involve relatively low diversification.” Charlie Munger is right: to the extent you believe you have a reliable method of constructing a high alpha “active” portfolio, less diversification is desirable.
In the spirit of aspiring to high conviction, we construct our portfolios to hold around 60 securities, on average.
Consider our typical process:
- Identify Investable Universe: We typically generate 1,200 names in this step of the process.
- Generic Momentum Screen: Select the top decile of firms on their past momentum, or 120 stocks.
- Quality of Momentum Screen: Select high-momentum firms with smoothest momentum, 60 stocks or 50%.
- Seasonality Screen: Rebalance the portfolio near the beginning of quarter-end months.
- Invest with Conviction: We invest in our basket of 60 stocks with the highest quality momentum.
We outline the historical results for the process outlined above. Like all historical results, we must take them with a grain of salt.
Figure 6 below shows the final result of our process. Column 1 is our Quantitative Momentum strategy that follows Steps 1 through 4; column 2 is the strategy that follows Steps 1 to 3, but dismisses seasonality; and column 3 follows Steps 1 through 2 and dismisses quality momentum. The returns shown below are net of 1.00% annual transaction costs (4 rebalances a year @ 0.25% a rebalance) and a 1.00% management fee. The SP500 index is gross of fees.
Each element of our Quantitative Momentum process increases the system’s overall effectiveness in expectation. However, regardless of how we build our momentum process, we must acknowledge that all momentum strategies have higher volatility and drawdowns than the passive market index. That is the nature of the beast. Yet while our Quantitative Momentum strategy is no different from a risk standpoint, for each unit of additional risk associated with our QM strategy, we are well compensated via much higher expected returns–a desirable quid pro quo.
Why Isn’t Everyone Doing This?
We believe our Quantitative Momentum process is evidence-based and has a chance to outperform the market over the long-haul on a risk-adjusted basis. But while all of this may sound promising, one must consider a simple question:
Why aren’t all investors doing it?
There are two key reasons (there are others), which we explain below:
- Momentum investing attracts less capital than traditional Value or Growth investing.
- High-conviction momentum investing is loaded with career risk for asset managers.
Why might momentum attract less capital? One reason is that momentum investing doesn’t easily fit in the standard “style” chart. Consider your typical chart from Morningstar, as depicted below in Figure 7.
Figure 7 highlights an important issue regarding momentum investing—it doesn’t fit into the standard classification table. It is a round peg people want to put into a square hole. Also, some simply misconstrue it. For instance, a common knee-jerk reaction is that momentum investing is just growth investing—but not so fast. From 1974-2014 we examine the overlap between the top decile of firms formed on their generic momentum (simple 12_2 momentum) and firms in the bottom decile when ranked on enterprise multiples (e.g., growth firms). Surprisingly, there is only a 28% overlap between the top decile of high momentum firms and growth firms (top decile) from 1974-2014. So while related to growth, momentum investing is decidedly not the same as growth investing—A momentum stock can be a value stock, a growth stock, or anything in between.
Unfortunately, style block mentality, which identifies a manager’s benchmark also affects the incentives of asset managers. Managers tend to create products that closely follow benchmark portfolios associated with the boxes above and avoid direct exploitation of the momentum anomaly documented in the academic literature.
Another related reason why many professionals shy away from momentum investing is the return path itself—the volatility and deviations from standard benchmarks are extreme. As was shown in Figure 6, the standard deviation of the market (S&P 500) was 15.40%, whereas the standard deviation of the Quantitative Momentum strategy is 25.38%. That is a nearly 65% increase in volatility! That added volatility is hazardous to an asset manager’s employment. For those who follow an index, the Quantitative Momentum strategy comes with a tracking error of slightly over 17%. In other words, prepare for major deviations from standard benchmarks and multiple opportunities to get fired as an asset manager.
The ability to withstand short-term pain is required to pursue a high-conviction momentum strategy, but the rewards for a disciplined investor are outsized upside expected returns.
In the short-run, most of us simply cannot endure the pain that momentum investing strategies impose on our portfolios and our psyches. It is simply too difficult. Furthermore, for those in the investment advisory business, providing a strategy with the potential for multi-year underperformance is akin to career suicide. And yet, at Alpha Architect, we explicitly focus on a momentum investing philosophy because the evidence for outperformance is so striking and robust. Why would we risk such career suicide? Our hope is that we can educate investors with the appropriate temperament on what it takes to achieve long-term investment success as a momentum-investor. It is not easy, and it is not for everyone, but for those rare souls who understand the discipline required, our systematic momentum investment process allows investors to simply “follow the model” and avoid behavioral biases that can poison even the most professional and independent fundamental momentum investors.
Our enhanced process can be distilled into the following phrase:
“Buy stocks with the highest quality momentum.”
 Gilovich, E., R. Vallone, and A. Tversky, 1985, The Hot Hand in Basketball: On the Misperception of Random Sequences, Cognitive Psychology, 17, pg. 295-314.
 Lehman, B. N., 1990, Fads, Martingales, and Market Efficiency, The Quarterly Journal of Economics, 105, pp. 1-28 and Jegadeesh, N., 1990, Evidence of Predictable Behavior of Security Returns, The Journal of Finance, 45, pp. 881-898.
 Jegadeesh, N., and S. Titman, 1993, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, The Journal of Finance, 48, pp. 65-91.
 We use overlapping portfolios. An example of a 3-month hold portfolio would be as follows: on Jan 1, buy the top decile and hold until March 31; on Feb 1, buy the top decile and hold until April 30; on March 1, buy the top decile and hold until May 31. So the portfolio return during March would be the equal-weighted basket of the stocks added on Jan 1, Feb 1, and March 1.
 Returns for momentum strategies can run back until 1927. We show results in this document starting in 1974 to (1) compare to our Quantitative Value strategy and (2) ensure the tradability of the strategy.
 Figure 3 also highlights that the alpha for a long/short momentum strategy decreases as the holding period increases (less rebalances). A similar result is found for long-only portfolios in many academic papers.
 Da, Z., U. G. Gurun, and M. Warachka, 2014, Frog in the Pan: Continuous Information and Momentum, Review of Financial Studies, pp. 1-48.
 The exact variable used is ID = sign(momentum over past 12 months ignoring last month)*(%negative-% positive)
 While the January finding in the paper is interesting (low momentum outperforms high momentum in January), attempting to trade on this can be difficult to implement. As such, we do not include this in our Quantitative Momentum screening methodology.
 This portfolio is rebalanced at the close on the last trading day of February, May, August, and November.
 The results associated with overlapping “no seasonality” portfolios are likely overstated relative to the seasonality portfolio because of increased complexity and transaction costs.
Performance figures contained herein are hypothetical, unaudited and prepared by Alpha Architect; hypothetical results are intended for illustrative purposes only.
Past performance is not indicative of future results, which may vary.
There is a risk of substantial loss associated with trading commodities, futures, options and other financial instruments. Before trading, investors should carefully consider their financial position and risk tolerance to determine if the proposed trading style is appropriate. Investors should realize that when trading futures, commodities and/or granting/writing options one could lose the full balance of their account. It is also possible to lose more than the initial deposit when trading futures and/or granting/writing options. All funds committed to such a trading strategy should be purely risk capital.
Hypothetical performance results (e.g., quantitative backtests) have many inherent limitations, some of which, but not all, are described herein. No representation is being made that any fund or account will or is likely to achieve profits or losses similar to those shown herein. In fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently realized by any particular trading program. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or adhere to a particular trading program in spite of trading losses are material points which can adversely affect actual trading results. The hypothetical performance results contained herein represent the application of the quantitative models as currently in effect on the date first written above and there can be no assurance that the models will remain the same in the future or that an application of the current models in the future will produce similar results because the relevant market and economic conditions that prevailed during the hypothetical performance period will not necessarily recur. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results, all of which can adversely affect actual trading results. Hypothetical performance results are presented for illustrative purposes only.
Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.
There is no guarantee, express or implied, that long-term return and/or volatility targets will be achieved. Realized returns and/or volatility may come in higher or lower than expected.
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