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Explaining and Predicting Momentum Performance Shifts Across Time and Sectors

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Listed:
  • Konstantinos Mamais
  • Dimitrios Thomakos
  • Prodromos Vlamis

Abstract

In this paper, we analyze the momentum of the NASDAQ and its major sectoral components across an extended period of key economic events, which include recessions, expansions, wars, financial crises, and the Covid‐19 health crisis. We seek to explain how momentum works as an investment strategy during different economic conditions and whether understanding how it works in‐sample can contribute to the out‐of‐sample forecasting of future financial performance. The novelty of our approach rests in the identification and exploitation of momentum characteristics that lead to the ranking of sectors depending on the period of economic activity that we are in. These rankings are tested and found to be robust for the in‐sample and the out‐of‐sample forecasting of financial performance, thus leading us to surmise that one can use the identification of past economic conditions to extrapolate for investing accordingly in the future. Our results indicate that this suggested approach works very well in practice and is, thus, a viable and fully interpretable investment strategy.

Suggested Citation

  • Konstantinos Mamais & Dimitrios Thomakos & Prodromos Vlamis, 2025. "Explaining and Predicting Momentum Performance Shifts Across Time and Sectors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 960-977, April.
  • Handle: RePEc:wly:jforec:v:44:y:2025:i:3:p:960-977
    DOI: 10.1002/for.3232
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    References listed on IDEAS

    as
    1. Barroso, Pedro & Santa-Clara, Pedro, 2015. "Momentum has its moments," Journal of Financial Economics, Elsevier, vol. 116(1), pages 111-120.
    2. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    3. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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    Cited by:

    1. Irene Aldridge, 2026. "Evaluating AI Investment Strategies," Papers 2606.08791, arXiv.org.

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