Quantitative Momentum Research: Intermediate-Term Momentum

////Quantitative Momentum Research: Intermediate-Term Momentum

Quantitative Momentum Research: Intermediate-Term Momentum

By |2017-08-18T16:58:26+00:00January 6th, 2015|Momentum Investing Research|

Return to Buying Winners and Selling Losers: Implications for Stock Market Efficiency

Abstract:

This paper documents that strategies that buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over three- to twelve-month holding periods. The authors find that the profitability of these strategies are not due to their systematic risk or to delay ed stock price reactions to common factors. However, part of the abnormal returns generated in the first year after portfolio formation dissipates in the following two years. A similar pattern of returns around the earnings announcements of past winners and losers is also documented.

Core Idea:

The ground-breaking work of Jegadeesh and Titman (1993) attracted academic attention to “Momentum”, or “Relative Strength” strategies. You might want add this paper to your “must-read list” if you are a fan of Momentum.

The authors demonstrate that a “Momentum” strategy (buying past “winners” and selling past “losers”, zero-cost portfolio) performs well for an intermediate-term horizon (3-12 months). They test this effect by constructing J-month/K-month strategies: select stocks based on past J months’ returns and hold the position for K months (J=3,6,9,12; K=3,6,9,12). In total they test 16 strategies.

Their main findings are:

  1. Selecting stocks based on past 12 months performance and holding the position for 3 months (12-month/3-month strategy, with one week lag) is the most successful strategy.
  2. The momentum premiums are not permanent, and they start experiencing negative abnormal returns around 12 months after the formation date, and dissipate within 2 years. These results suggest a long-term reversal.
  3. Seasonal effect and Earning announcement effect also found.
    • Seasonal Effect: Momentum strategies experience negative returns in January, but achieve positive abnormal returns in other months. What’s more, momentum is weak in August but works particularly good in April, November, and December.
    • Earning Announcement Effect: For the first 7 months after formation, past winners yield consistently higher returns around earnings announcements than do past losers. But in the following 13 months, past losers perform better.
  4. Profitability of these strategies are not due to their systematic risk or to lead-lag effects, but rather due to delayed price reactions to firm-specific information.
    • Market underreacts to information about the short-term prospects (such as earning announcement) of firms but overreacts to information about the long-term prospects.
2014-11-13 12_08_20-Momentum academic Research recap_V01.pptx - Microsoft PowerPoint (Product Activa

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. Additional information regarding the construction of these results is available upon request.

Click here to read our previous post:

Recommending the Trend: a Behavioral Basis for Momentum Strategies


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About the Author:

Jack Vogel, PhD
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.
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