This post is the second and final portion of the review on momentum published on Momentum literature. The seminal article on momentum was published by Jegadeesh and Titman in 1993. Although the Jegadeesh article foreshadowed much of the research on cross-sectional and time series momentum at the stock level, it wasn’t until the mid-to-late-2000s that investigators turned to study momentum at the industry and factor levels. Industry and factor momentum should be viewed as recent developments in the wider momentum story, although these aggregated measures of momentum lack any theoretical foundation.

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?

Consider the behavioral explanation of momentum that argues past winners outperform losers due to mispricing as investors either under- or overreact to news about the stock. Is that explanation sufficient to also explain the co-movement in returns among winners and losers in a future period documented by the research? A new branch of the literature is necessary to capture and describe the factor structure that ties firm-specific momentum together with industry and factor momentum. 

What are the Academic Insights?

There are a number structural drivers of industry and factor momentum that have been proposed or examined in the literature:

1. A channel that drives positive autocorrelation of factor momentum amplified by variability in the stock’s exposure to that factor.  Gupta and Kelly (2019) identified positive first-order autoregressive time-series structures in factors. A factor momentum strategy (combining 65 factors) produced a Sharpe ratio of 0.84, robust to FF risk factors, static factor returns, and industry momentum.  It was not robust to short-term reversals and therefore did not dominate stock-level momentum. Similar results were found by Ehsani and Linnainmaa (2022a), and also document a model of investor persistent sentiment that is sufficient and persistent, investors will mitigate demand beyond that of a rational investor and produce autocorrelation in factor returns. These results are consistent with risk-based explanations because it requires arbitrageurs to continue to carry high systematic risks.

2. A channel whereby autocorrelation in returns is unexplained by factor exposure. The research is split on whether or not factor momentum subsumes all other momentum structures including cross-sectional momentum, industry momentum and intermediate horizon momentum. In some cases (Blitz et al. (2011)), found residual return measures of momentum outperform raw return measures and conclude firm-specific events drive momentum and not autocorrelated factors. There is consistent evidence that 1-year stock momentum is largely driven by factor momentum (Ehsani and Linnainmaa, 2022a, Arnott et al. 2021). However the question why stock returns reverse in the short-term, whereas monthly factor returns exhibit strong positive first-order autocorrelation remains unanswered.

3. Most of the research on industry momentum concludes that stock momentum is only partially driven by industry momentum.  However, the positive results that do occur to industry momentum strategies were subject to the use of a 1-month look-back and 1-month holding period.  Overall, it is much too early to declare industry momentum as anything but a very small source of stock momentum.

Why does it matter?

The empirical evidence on momentum developed over the last 30 years has served to provide a theoretical foundation for reliable and profitable investment strategies implemented across a variety of asset classes. The average monthly return across geographies and assets are presented in Figure 1. They range from 0.41% for US equity to a high of 1.22% for Commodity futures. While those numbers do not account for industry or factor forms of momentum, they at least tell a theoretically sound story as either a behavioral bias or a systematic risk factor. With factor and industry momentum, we also observe significant strategy returns but without an accompanying theoretical explanation. Certainly, reason enough for factor or industry momentum to remain a prominent topic in finance in the future. There are a number of open questions. For instance, why do momentum stocks rise and fall in unison in future periods if there is no pervasive systematic risk in common? Do the factor structures common to individual stocks differentiate between behavioral biases or risk-based explanations of momentum? Or what is the extent to which momentum in stocks is driven by an exposure to systematic risk factors, industry factors, or firm-specific events, all of which themselves exhibit serial correlation.

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.

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