In this article, the author examines the research published over the last 30 years on momentum and its theoretical credibility. One of the original momentum articles was published by Jegadeesh and Titman in 1993, and is considered the seminal work on the topic. The research review contained in this publication begins with the 1993 work and confines itself to only the highest quality journals among the plethora of work that has been published on momentum.

Momentum: what do we know 30 years after Jegadeesh
and Titman’s seminal paper?

  • Tobias Wiest
  • Financial Markets and Portfolio Management
  • A version of this paper can be found here
  • Want to read our summaries of academic finance papers? Check out our Academic Research Insight category.

What are the research questions?

  • This article provides an excellent review of at least 60 high quality articles that investigate the momentum topic. The quality of the journals speaks volumes as 47 articles were published in the Journal of Finance, Journal of Financial Economics or the Review of Financial Studies. The overview is meticulous and insightful.
  • The paper is divided into a number of sections. First, an outline of the original cross-sectional momentum study by Jegadeesh and Titman (1993).  Second, a review of important variations on cross-sectional momentum. Third, an analysis of the origin of the profits from momentum strategies. Fourth, factor and industry momentum as recent developments in the wider momentum story. Although these aggregated measures of momentum lack any theoretical foundation, the implications for portfolio construction are interesting. A forthcoming summary will cover the research on factor and industry momentum.

What are the Academic Insights?

  1. The original article by Jegadeesh and Titman published in 1993 is considered the foundational approach adopted by a large number of momentum articles published thereafter.  There are 5 basic components: (i) every month calculate the cumulative return over a formation period usually between 3 month, 6 month, 9 month and 12 month periods; (ii) sort stocks into deciles based on the cumulative return; (iii) create equally weighted portfolio returns over the future holding period; (iv) calculate the strategy return as the return spread between the highest (long) past return portfolio and lowest (short) past return portfolio; (v) finally, most studies omit the 1-month between the formation and holding period in order to eliminate any short-term reversal effects.  This is referred to as the “cross-sectional” momentum strategy.  It has produced positive long/short spreads that are statistically significant for all 3-, 6-, 9-, and 12-month formation periods and holding periods.
  2. Time-series momentum vs. the original cross-sectional approach. Time-series (TS) momentum scaled by volatility was first tested by Moskowitz et al. (2012) across 58 asset classes (equity indices, commodities, currencies, and government bond futures) between 1965 and 2009.  The recent 12-month return was found to predict the future 1-month return. A long position (stocks with the highest past 12-month return) vs. a short position (stocks with the lowest past 12-month return) trading strategy was not only statistically significant for 52 of the asset classes, but outperformed the cross-sectional (CS) momentum strategy.  The results were robust to common risk adjustments. 
  3. Results of TS strategies were challenged by researchers (Goyal and Jegadeesh, 2018) on the basis of the higher leverage present in TS strategies. When CS momentum is adjusted for differences in leverage, the difference in performance was eliminated.  A leveraged, that is, volatility scaled CS strategy for international asset classes was the exception, as those portfolios exhibited superior performance. Researchers speculated that superiority was due to very large allocations to bonds, an asset class that is typically low volatility and low excess returns.
  4. Other variations of note include construction of a residual measure of momentum, an intermediate measure and a dynamic measure. Blitz et al. (2011) managed to reduce the volatility of the 12-month strategy by 50%. In that variation, raw returns were replaced with residuals obtained from applying the Fama/French 3 factor model.  The strategy produced an increase in the Sharpe ratio from a .45 baseline to .90.  Novy-Marx (2012) used return data from 1927-2010 to create an intermediate strategy that replaced the prevailing 12-month less 1-month formation period approach. The formation period of t-12 months to t-7 months produced momentum profits of 1.20% monthly after adjusting for Fama/French factors and Carhart (1997) momentum.  The dynamic momentum strategy was tested in response to momentum “crashes”.  In times of high market volatility that tend to follow bear markets a scheme of scaling based on a conditional Sharpe ratio resulted in an actual Sharpe ratio of 1.2 relative to a 0.68 baseline (Daniel and Moskowitz (2016). The conditional volatility measure being estimated by a GARCH model.  Similar results were found by Barroso and Santa-Clara (2015) and Moreira and Muir (2017) using a realized volatility approach. Momentum portfolios appear to benefit from some type of volatility scaling.
  5. Is the momentum effect the result of a behavioral bias on the part of investors or simply another measure of risk we should take into account? There has been no resolution of this issue in the literature. However, two camps have crystallized around behavioral and risk-based explanations. Behavioral drivers of momentum generally emphasize underreaction, overreaction, overconfidence or disposition biases on the part of investors.  The explanations argue that those biases influence trading behavior that ultimately drive stock prices away from intrinsic value and are corroborated by autocorrelation of stock returns.  The errors in pricing are gradually reversed as public information begins to affect trading and drive prices toward intrinsic value over the longer term.  If investors are overconfident or overreact, they may buy a stock based on their own private information.  If that initial reaction is corroborated by public information, investors will continue to drive the price up assuming their skill is responsible.  A number of researchers are associated with the overreaction and overconfidence viewpoints including Daniel et al. (1998), Jegadeesh and Titman (1993) and De Bondt and Thaler (1985).  More recently, some creative variations on momentum were developed.  For example, Hillert et al. (2014) constructed a firm-based measure of media coverage and reported momentum returns were 3 times higher when stocks were screened for the highest quintile of the media coverage measure.  Controlling for size, analyst coverage and index membership, the momentum strategy returned 1.02% vs. a 0.33% baseline. Media coverage appears to be a valuable enhancement to constructing momentum portfolios. Switching gears to the underreaction bias: Momentum as the result of underreaction may occur if investors fail to incorporate information into prices via their trading behavior.  Barberis et al. (1998) argues that underreaction may occur if investors are conservative and underweight new information, thus driving stock prices away from intrinsic value.
  6. Is momentum compensation for greater risk? In the realm of rationality and the theory of efficient markets, momentum returns are compensation for bearing risk.  That statement makes considerable sense in light of the long-term persistence of the momentum effect.  However, the question still remains.  Why would a stock with high or low momentum be more or less risky fundamentally speaking?  Johnson (2002) provides evidence that stock prices are directly correlated with changes in growth rates.  If growth rate shocks are distributed unevenly among high and low momentum stocks, then momentum is simply a proxy for growth rates.  We would expect to see stronger momentum in stocks with high book-to-market ratios.  If book-to-market is a proxy for a ”growth rate risk factor”, then it can be argued that momentum is compensation for that risk factor and the argument is settled.

Why does it matter?

The empirical evidence supporting the existence of momentum in asset prices has had far-reaching effects on investment practice and theory. (and a reason why Alpha Architect still invests in momentum) This evidence provides the foundation for a reliable and profitable investment strategy implemented across a variety of asset classes, by a variety of investors including mutual funds, ETFs, hedge funds, traders and the retail community. The average monthly return across geographies and assets are presented in Figure 1. The bad news is that the mere existence of momentum profits on such a wide-scale basis challenges academic theory at its most basic level–that of weak form market efficiency.  Fama said it best:   

“Momentum is the biggest embarrassment to the theory” and “I wish it would go away”.

All jokes aside, Fama’s interview can be viewed here – INVESTORS FROM THE MOON: FAMA (

The most important chart from the paper

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 and do not reflect management or trading fees, and one cannot invest directly in an index.


For over 30 years, extensive research has found corroborating evidence that past winners continue to yield higher returns than past losers. This momentum effect is robust across various asset classes and across the globe and presents perhaps the most pervasive contradiction of the efficient market hypothesis. This article reviews three strands of literature on momentum. First, I outline the construction of momentum strategies, emphasizing improvements and alternatives such as time-series momentum, residual momentum, and risk-managed momentum. Second, I summarize the most prominent behavioral-based and risk-based explanations for the origin of momentum. Finally, I present in detail the findings on commonality in stock momentum, namely on industry and factor momentum.

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About the Author: Tommi Johnsen, PhD

Tommi Johnsen, PhD
Tommi Johnsen is the former Director of the Reiman School of Finance and an Emeritus Professor at the Daniels College of Business at the University of Denver. She has worked extensively as a research consultant and investment advisor for institutional investors and wealth managers in quantitative methods and portfolio construction. She taught at the graduate and undergraduate levels and published research in several areas including: capital markets, portfolio management and performance analysis, financial applications of econometrics and the analysis of equity securities. In 2019, Dr. Johnsen published “Smarter Investing” with Palgrave/Macmillan, a top 10 in business book sales for the publisher.  She received her Ph.D. from the University of Colorado at Boulder, with a major field of study in Investments and a minor in Econometrics.  Currently, Dr. Johnsen is a consultant to wealthy families/individuals, asset managers, and wealth managers.

Important Disclosures

For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice. Certain information is deemed to be reliable, but its accuracy and completeness cannot be guaranteed. Third party information may become outdated or otherwise superseded without notice.  Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency has approved, determined the accuracy, or confirmed the adequacy of this article.

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. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).

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