The Quantitative Momentum Investing Philosophy

The Quantitative Momentum Investing Philosophy

December 1, 2015 $GMOM, $mtum, Introduction Course, Key Research, Momentum Investing, White Papers
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The Quantitative Momentum Investing Philosophy

Buy Stocks with the Highest Quality Momentum

  • Author: Jack Vogel, Ph.D., and Wesley R. Gray, Ph.D.
  • PDF version

Executive Summary

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.”[1]

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.[2] 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.

Introduction

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):

  1. Identify Investable Universe: Our universe generally consists of mid- to large-capitalization U.S. exchange-traded stocks.
  2. Generic Momentum Screen: We rank stocks within our universe based on their past twelve-month returns, ignoring the first month.
  3. 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.
  4. Momentum Seasonality Screen: We take advantage of certain seasonal aspects applicable to momentum investing, which determines the timing of our rebalance.
  5. 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.

The Quantitative Momentum (QM) Process

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.[3] 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[4], and Brett S. Green and Jeffrey Zwiebel in 2013[5], 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[6]
  • Long-Term Momentum (3 to 5 years) – exhibits a reversal in returns[7]
  • Intermediate-Term Momentum (6-12 months) – exhibits a continuation in returns[8]

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.[9] 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 12/31/2014.[10]

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 0.80% annual transaction costs (4 rebalances a year @ 0.20% per rebalance) and a 1.00% management fee. The SP500 index is gross of fees.

The Quantitative Momentum Investing Philosophy_a generic mom screen
The results are hypothetical results and 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.

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. [11]

The Quantitative Momentum Investing Philosophy_Da, Gurun and Warachka
The results are hypothetical results and 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.

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.[13] 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 0.80% annual transaction costs (4 rebalances a year @ 0.20% a rebalance) and a 1.00% management fee. The SP500 index is gross of fees.

The Quantitative Momentum Investing Philosophy_quality of mom screen
The results are hypothetical results and 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.

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.[14] 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.[15]  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)[16] to a portfolio to a portfolio that ignores seasonality (column 2). The returns shown below are net of 0.80% annual transaction costs (4 rebalances a year @ 0.20% a rebalance) and a 1.00% management fee. (There are competing arguments for transaction costs here and here).

The Quantitative Momentum Investing Philosophy_seasonality adjustment
The results are hypothetical results and 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.

As Figure 5 shows, forming the portfolio to exploit seasonality effects yields the highest CAGR, Sharpe and Sortino ratios.[17]

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:

  1. Identify Investable Universe: We typically generate 1,200 names in this step of the process.
  2. Generic Momentum Screen: Select the top decile of firms on their past momentum, or 120 stocks.
  3. Quality of Momentum Screen: Select high-momentum firms with smoothest momentum, 60 stocks or 50%.
  4. Seasonality Screen: Rebalance the portfolio near the beginning of quarter-end months.
  5. 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 0.80% annual transaction costs (4 rebalances a year @ 0.20% a rebalance) and a 1.00% management fee. The SP500 index is gross of fees.

The Quantitative Momentum Investing Philosophy_Seasonal smooth momentum
The results are hypothetical results and 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.

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:

  1. Momentum investing attracts less capital than traditional Value or Growth investing.
  2. 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.

The Quantitative Momentum Investing Philosophy_typical style investment chart

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 15.45%, whereas the standard deviation of the Quantitative Momentum strategy is 25.59%. 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.

Conclusion

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.[18] 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.”

 

Footnotes:

[1] Fama, E. and K. French, 2008, Dissecting Anomalies, The Journal of Finance, 63, pg. 1653-1678.

[2] Geczy, C. and M. Samonov, 212 Years of Price Momentum, University of Pennsylvania Working Paper, accessed 10/31/2015

[3] 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.

[4] Bocskocsky, A., J. Ezekowitz, and C. Stein, 2014, The Hot Hand: A New Approach to an Old “Fallacy”, working paper, accessed 11/15/15

[5] Green, B. S., and J. Zwiebel, 2015, The Hot-Hand Fallacy: Cognitive Mistakes or Equilibrium Adjustments? Evidence from Major League Baseball, working paper, accessed 11/15/15

[6] 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.

[7] DeBondt, W. F., and R. Thaler, 1985, Does the Stock Market Overreact?, The Journal of Finance, 40, pp. 793-805.

[8] 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.

[9] 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.

[10] 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.

[11] 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.

[12] Da, Z., U. G. Gurun, and M. Warachka, 2014, Frog in the Pan: Continuous Information and Momentum, Review of Financial Studies, pp. 1-48.

[13] The exact variable used is ID = sign(momentum over past 12 months ignoring last month)*(%negative-% positive)

[14] Sias, R., 2007, Causes and Seasonality of Momentum Profits, Financial Analyst Journal, 63, pp. 48-54.

[15] 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.

[16] This portfolio is rebalanced at the close on the last trading day of February, May, August, and November.

[17] The results associated with overlapping “no seasonality” portfolios are likely overstated relative to the seasonality portfolio because of increased complexity and transaction costs.

[18] High conviction momentum can be combined with high conviction value strategies to help mitigate portfolio risk.

 


 

Disclosure: 

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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Please remember that past performance is not an indicator of future results. Please read our full disclaimer. The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. This material has been provided to you solely for information and educational purposes and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The factual information set forth herein has been obtained or derived from sources believed by the author and Alpha Architect to be reliable but it is not necessarily all-inclusive and is not guaranteed as to its accuracy and is not to be regarded as a representation or warranty, express or implied, as to the information’s accuracy or completeness, nor should the attached information serve as the basis of any investment decision. No part of this material may be reproduced in any form, or referred to in any other publication, without express written permission from Alpha Architect.

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About the Author

Jack Vogel, Ph.D.

Jack Vogel, Ph.D., conducts research in empirical asset pricing and behavioral finance, and is a co-author of DIY FINANCIAL ADVISOR: A Simple Solution to Build and Protect Your Wealth. His dissertation investigates how behavioral biases affect the value anomaly. His academic background includes experience as an instructor and research assistant at Drexel University in both the Finance and Mathematics departments, as well as a Finance instructor at Villanova University. Dr. Vogel is currently a Managing Member of Alpha Architect, LLC, an SEC-Registered Investment Advisor, where he heads the research department and serves as the Chief Financial Officer. He has a PhD in Finance and a MS in Mathematics from Drexel University, and graduated summa cum laude with a BS in Mathematics and Education from The University of Scranton.


  • TheChrisp1231

    Looks good guys , I’m keen to get some access to the momentum premium ! , I thought you guys had mentioned that you can add drawdown reducing techniques/hedging to momentum portfolios but not to value .So i was surprised to see none applied above .

    Also do you guys have the yearly returns ? , be interesting to see the differences in generic , low quality and high through different periods ( the dot com,)

  • Hello Jack,

    Aren’t the high drawdown levels in excess of 60% of concern and maybe this is an answer to the question: Why aren’t all investors doing it?

    Add slippage and other friction effects and drawdown can get even larger.

    It appears that these strategies are too sensitive to volatility spikes and clustering.

  • Jack Vogel, PhD

    Yes, the portfolio above is 100% invested, so there are no stop-loss rules. The stop-loss rules can help to attempt to lower drawdowns but we prefer to use these outside of the security selection process.

    Here are the annual returns:

  • J K

    Hello,

    This is very interesting. Thanks for the findings!

    But how does the “frog-in-the-pan” phenomenom relate to the findings of “momentum acceleration” studies, e.g.
    http://www.cxoadvisory.com/492/momentum-investing/buying-on-impulse-change-in-momentum/

    In addition to that study, there are also additional studies suggesting that stocks with high price acceleration outperform stocks with low price acceleration.

    Isn’t that somewhat in conflict with “frog-in-the-pan” anomaly?

  • TheChrisp1231

    Thanks Jack , one more question: was a similar premium ( quality momentum over generic) observed in international stocks ?

  • Jack Vogel, PhD

    In our tests, yes.

  • Jack Vogel, PhD

    I can’t view the paper on SSRN, can you send the link?

  • JAK78

    Are you at all concerned about excessive data mining and overfitting your data by having multiple screens?

  • Jack Vogel, PhD

    No, we are not optimizing a model and loading on factors that worked the best. We use a screening methodology takes 3 steps, all backed by a sound theory and then academic evidence. Step 2 sorts to the top 10% of firms on momentum — there appears to be a continuation of returns for past winners and losers (Jegadeesh and Titman 1993). Step 3 examines only the top half of the top 10% of momentum firms based on the path of their momentum, where we look for high-momentum firms with a “smoother” path. The theory is that these firms are more likely to be overlooked, and have a larger continuation in returns, as in Da, Gurun, and Warachka 2014. Step 4 examines the seasonality component of momentum. The theory is that window dressing causes mutual fund managers to purchase the best-performing stocks to show this in their portfolio at the end of the quarter. The evidence in Sias 2007 shows that the momentum portfolios perform the best in quarter ending months — so we use this to time our rebalance.

    I hope that helps!

  • Doug

    Nice work! A few questions/comments from the peanut gallery:

    1) Why exclude REITs? To make the overall asset allocation decision cleaner? (i.e. if you already have 10% in VNQ, then you won’t have an extra 1%-3% sneaking into your portfolio via QMOM?)
    2) I’m concerned that the seasonal anomaly is ripe for the picking by specialized funds, and may get arbed away in the future.
    3) Right now, Large Cap Growth Funds (NOT growth “stocks” but “funds”) have a big momentum component. MTUM has closely tracked VRGWX (Vanguard Russell 1000 Growth Fund) since inception. That may change, but Large Cap Growth has been a cheap substitute for momentum over the past 2-3 years.

    Despite the quibbles, I’m looking forward to QMOM/IMOM launch!

    GO NAVY BEAT ARMY!

  • Great work, as always!

  • J K

    Helli Jack, seems that the paper has been removed from SSRN, I can’t find it anywhere either. Sorry!

    But here are two similar papers on the same (momentum acceleration) topic. And it looks to me as if they don’t really support the “frog-in-the-pan” theory. Maybe you could comment these? Thanks.

    Momentum acceleration:

    http://www.google.fi/url?q=http://www.efmaefm.org/0EFMAMEETINGS/EFMA%2520ANNUAL%2520MEETINGS/2005-Milan/papers/43-casavecchia_paper.pdf&sa=U&ved=0ahUKEwjItuyBuLvJAhWKBiwKHVzLCv0QFggNMAE&sig2=0OsTDTSdj1p0WkAYmen2dg&usg=AFQjCNGJo5iQwJRUvYoR8iqrv0LnlfCbmA

    Investor attention, visual price pattern and momentum:

    http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2292895

  • Jack Vogel, PhD

    Yes, this strategy has a large tracking error, and in the past had a larger drawdown than the market.

  • Steve

    Jack, I think I’ve sent to Wes before – will email again. See the post you guys did a couple days ago on momentum, I talk about this in one of my comments to that thread.

    To J K – I don’t believe they relate (directly). Acceleration is different to return consistency.

    Also, the way Wes/Jack prefer to rebalance (ie. more frequently), they might not find as much benefit to acceleration. There’s a paper (Ibbotson) from memory (recent one)…that shows acceleration as a bad thing, but that’s because of the monthly rebalance – and the paper uses it as an explanation for short term reversal.

    However, the Marks paper shows Acceleration as useful on a longer holding period (because the benefits come later. i.e. Accelerating momentum stocks don’t actually do better in the shorter term, its later on that they do). That’s what makes it interesting for the investor trying to hold on to that magic one year mark (also a reason to use 3 or 6 month momentum rather than 12 month).

    Also – my opinion – I think the Moving Average ratio paper (Parkes, from memory) inadvertently also shows the acceleration idea.

    There is also one other paper on this that goes really well with the original Marks paper – trend salience, by some Aussie researchers.

    http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2395718

    Jack – I’d love to see the AA team do your thing to examine this idea from the real world perspective of investors! Can we twist your arm? I’ve not yet seen any papers do a back up study of trend salience / acceleration on any other markets – so I’ve still got the usual lack of confidence when only tested on one market.

  • Steve

    Monster post! Congrats guys, on pinning down your strategy after all the research – and presenting it in such a methodical manner. Love the symmetry with QV!

  • Jack Vogel, PhD

    Both those papers are interesting, however the frog-in-the-pan paper is in one of the top 3 Finance Journals (Review of Financial Studies). We did test a related paper to the ones you listed (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2652592 ) and found frog-in-the-pan momentum was better (when comparing the long-only strategies on mid/large cap stocks).

    All else equal, just being in the top decile of stocks formed on momentum was a good bet in the past. All of these papers are trying to find a better way to sort “within” the top decile — a noble goal! We like the frog-in-the-pan measure as it is backed by a behavioral theory (people underreact to certain stocks, those with less attention).

  • Eric Darnel

    Even according to your own research, value stocks offer higher returns with lower standard deviation. So I wonder, why bother with momentum?

  • Mark

    What if you start with step 3, followed by step 2?

  • Jack Vogel, PhD

    In general, value and momentum tend to work well at different times, so the overall volatility can be decreased in a portfolio by combining the two (however still slightly higher than standalone value). Here is a blog we wrote on combining value and momentum

    http://blog.alphaarchitect.com/2015/03/26/the-best-way-to-combine-value-and-momentum-investing-strategies/

  • Jack Vogel, PhD

    It is better to do 2 and then 3

  • J K

    Jack, thanks a lot for your comments, and Steve, for your additional insights (as well as the interesting papers). Would be interesting indeed to see a back up study of trend salience / acceleration.

    Thanks again, AA team, for the great work!

  • Steve

    Just pushing the question – not sure if you looked at it, but if you did…what about the seasonality in international markets? I wonder, because not everywhere has quarterly reporting.

  • Jack Vogel, PhD

    in our tests, yes on the end of quarter effect. No on the January effect.

  • Steve

    Thanks Jack – that’s really interesting. I knew about the Jan effect being US centric…but I didn’t expect that answer re: quarter effect. Hmmm…

  • Anthony Ialeggio

    Great post! The drawdowns seem to be the biggest concern here, as others have pointed out. I use a dual momentum approach in my portfolio to limit downside in each asset class. Do you think that would work well if I employed this approach in my equity allocation? Thanks.

  • If you want to avoid equity-like drawdowns on a long-only strategy you need to deploy some sort of market-timing mechanism as an overlay. Dual momentum is certainly a reasonable approach, but there are also others: http://blog.alphaarchitect.com/2015/02/13/asset-allocation-horserace-robust-asset-allocation-raa-vs-dual-momentum/

  • Thomas

    Have you looked into applying dual momentum to QMOM and IMOM ? I.e., simply focusing on equities, e.g., QMOM or IMOM / BND or BNDX ?

  • Simon Fung

    Hi Jack,
    I read with great interest your good book : DIY Financial Advisor and learn a lot. But I am not sure I understand why in calculating the cumulative return, you ignore that for the last month ( page 146 refers). Your advice please.
    Simon

  • Jack Vogel, PhD

    Simon,

    Becasue there is a short-term reversal in returns from month to month. Here is a blog post we wrote on the topic: http://blog.alphaarchitect.com/2015/01/14/quantitative-momentum-research-short-term-return-reversal/

    Two papers document this, Lehmann (1990) and Jegadeesh (1990).

    Since we know there is a short term reversal in returns, we exclude this from our momentum calculation. That being said, if one includes the past month, the results are similar.

    I hope that helps.

  • Simon Fung

    Jack,
    Thank you very much for your quick return. When I try to DIY, I ran into another problem :
    When u mentioned risk free return of treasury bill in page 110 of your good book, are u refering to the yield of 1-year US treasury bill or 10-year treasury bond yield? Also in the last column of table 7.2, are u simply multiply the monthly returns to arrive at the 12-month cumulative return?
    Your advice please
    Tks
    Simon

  • Jack Vogel, PhD

    We use short-term T-bills return (1 or 3 months) and yes you multiply 1+ the return in the last column in Table 7.2

  • Henning Hammar

    Great post and a great new product. The momentum effect seems really interesting to use and especially in combination with value. Momentum has seemed to work well the past year, but looking at the past 10-15 years the effect seems to disappear for US stocks, something discussed on Jesse Livermores blog http://www.philosophicaleconomics.com/2015/12/momentum/ and that can be easily checked using Fama-French data. Do you see that effect in your data and why should momentum continue to work if not arbitraged away? Quantitative momentum seems easier to arbitrage away than quantitative value.

  • Hi Henning,
    Generic 2-12 momentum has been “blah” in the recent past (talking about the long-only strategy here). You still see benefits to a “quality momentum” strategy, however. And as the post above highlights, as well as the original frog-in-the-pan paper, the so-called momentum effect is driven by momentum stocks that steadily grind to get their momentum. So one could argue that generic 2-12 has never really worked, especially after controlling for those quality momentum stocks that actually drive the anomaly. At least for the time being, there isn’t much evidence that the “quality momentum” stock effect has been arbitraged away. If you layer in seasonality elements, the probability of the effect being completely arbitraged away gets even lower. Of course, anything is possible, but the intense volatility of momentum strategies will tend to keep a lot of arbitrage capital at bay…

  • Henning, forgot to mention a more wonky — but valid — argument that momentum hasn’t “lost”. The argument is one put forth by Asness et. al. Basically, even if momentum excess returns were ~0, they bring so much diversification benefits to a portfolio, they should still be considered “anomalous.”

    http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2435323

  • Henning Hammar

    Thanks for the quick reply. Interesting thoughts. It seems “quality momentum” has a certain benefit compared with regular momentum, especially in combination with value.

    Thanks as well for the paper, will have a look at it. I’m also looking forward to see your results for a risk-managed QMOM, especially due to the large drawdowns.

  • Hannibal Smith

    You still made the classic mistake of style boxitis! The opposite of value is NOT growth. The results at the other end of a screening filter is not the inverse opposite, just the lowest quality results of the filter. If you screen filter for growth properly, you should see that technical momentum is merely a sub-set of fundamental momentum. After all, what ultimately drives stock prices higher? Earnings.