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Time-series and cross-sectional momentum strategies under alternative implementation strategies

Author

Listed:
  • Ron Bird

    (Paul Woolley Centre, University of Technology Sydney, Ultimo, NSW, Australia; Waikato Management School, The University of Waikato, Hamilton, New Zealand)

  • Xiaojun Gao

    (Waikato Management School, The University of Waikato, Hamilton, New Zealand)

  • Danny Yeung

    (Finance Discipline Group, University of Technology Sydney, Ultimo, NSW, Australia)

Abstract
The study compares the performance of alternative implementations of both time-series and cross-sectional momentum strategies across 24 markets. We find that over our sample period, both types of momentum strategies generate positive returns under the majority of implementations evaluated but that time-series momentum is clearly superior. An important difference between the two momentum strategies is that with time-series momentum, the number of stocks included in the winner and loser portfolios vary with the state of the market. As a consequence, cross-sectional momentum digs deeper to select winning stocks when markets are weak and deeper to select losing stocks when markets are strong. As the information in the momentum signals is concentrated in the tails of the return distribution, it is not that surprising that momentum is best implemented using time-series momentum.

Suggested Citation

  • Ron Bird & Xiaojun Gao & Danny Yeung, 2017. "Time-series and cross-sectional momentum strategies under alternative implementation strategies," Australian Journal of Management, Australian School of Business, vol. 42(2), pages 230-251, May.
    • Handle: RePEc:sae:ausman:v:42:y:2017:i:2:p:230-251
    DOI: 10.1177/0312896215619965
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Papailias, Fotis & Liu, Jiadong & Thomakos, Dimitrios D., 2021. "Return signal momentum," Journal of Banking & Finance, Elsevier, vol. 124(C).
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    4. Simarjeet Singh & Nidhi Walia, 2020. "Time-Series And Cross-Sectional Momentum In Indian Stock Market," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 9(3), pages 161-176.
    5. Vo, Xuan Vinh & Truong, Quang Binh, 2018. "Does momentum work? Evidence from Vietnam stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 17(C), pages 10-15.
    6. Milan Fičura, 2019. "Forecasting Cross-Section of Stock Returns with Realised Moments," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2019(2), pages 71-84.
    7. Enrique Benavides Rosales, 2017. "Time-series and cross-sectional momentum and contrarian strategies within the commodity futures markets," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1339772-133, January.
    8. Simarjeet Singh & Nidhi Walia & Stelios Bekiros & Arushi Gupta & Jigyasu Kumar & Amar Kumar Mishra, 2022. "Risk-managed time-series momentum: an emerging economy experience," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, vol. 27(54), pages 328-343, November.
    9. Yang Gao & Henry Leung & Stephen Satchell, 2018. "A critique of momentum strategies," Journal of Asset Management, Palgrave Macmillan, vol. 19(5), pages 341-350, September.

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    More about this item

    Keywords

    Cross-sectional momentum; implementation; investment performance; market conditions; time-series momentum;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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