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Informed momentum trading versus uninformed "naive" investors strategies

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  • Banerjee, Anurag
  • Hung, Chi-Hsiou

Abstract

We construct a zero net-worth uninformed "naive investor" who uses a random portfolio allocation strategy. We then compare the returns of the momentum strategist to the return distribution of naive investors. For this purpose we reward momentum profits relative to the return percentiles of the naive investors with scores that are symmetric around the median. The score function thus constructed is invariant and robust to risk factor models. We find that the average scores of the momentum strategies are close to zero (the score of the median) and statistically insignificant over the sample period between 1926 and 2005, various sub-sample periods including the periods examined in (Jegadeesh and Titman, 1993) and (Jegadeesh and Titman, 2001). The findings are robust with respect to sampling or period-specific effects, tightened score intervals, and the imposition of maximum-weight restrictions on the naive strategies to mitigate market friction considerations.

Suggested Citation

  • Banerjee, Anurag & Hung, Chi-Hsiou, 2011. "Informed momentum trading versus uninformed "naive" investors strategies," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3077-3089, November.
    • Handle: RePEc:eee:jbfina:v:35:y:2011:i:11:p:3077-3089
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    References listed on IDEAS

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    Cited by:

    1. Ludovic Cal`es & Apostolos Chalkis & Ioannis Z. Emiris, 2021. "The cross-sectional distribution of portfolio returns and applications," Papers 2105.06573, arXiv.org.
    2. Apostolos Chalkis & Emmanouil Christoforou & Ioannis Z. Emiris & Theodore Dalamagas, 2020. "Modeling asset allocation strategies and a new portfolio performance score," Papers 2012.05088, arXiv.org, revised Sep 2021.
    3. Ludovic Cales & Apostolos Chalkis & Ioannis Z. Emiris & Vissarion Fisikopoulos, 2018. "Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises," Papers 1803.05861, arXiv.org.
    4. Clare, Andrew & Seaton, James & Smith, Peter N. & Thomas, Stephen, 2016. "The trend is our friend: Risk parity, momentum and trend following in global asset allocation," Journal of Behavioral and Experimental Finance, Elsevier, vol. 9(C), pages 63-80.
    5. Hung, Chi-Hsiou D. & Banerjee, Anurag N., 2014. "How do momentum strategies ‘score’ against individual investors in Taiwan, Hong Kong and Korea?," Emerging Markets Review, Elsevier, vol. 21(C), pages 67-81.
    6. Hung, Chi-Hsiou D. & Azad, A.S.M. Sohel & Fang, Victor, 2014. "Determinants of stock returns: Factors or systematic co-moments? Crisis versus non-crisis periods," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 14-29.
    7. Apostolos Chalkis & Emmanouil Christoforou & Ioannis Z. Emiris & Theodore Dalamagas, 2021. "Modeling asset allocations and a new portfolio performance score," Digital Finance, Springer, vol. 3(3), pages 333-371, December.
    8. Calès, Ludovic & Chalkis, Apostolos & Emiris, Ioannis Z., 2019. "On the cross-sectional distribution of portfolio returns," Working Papers 2019-11, Joint Research Centre, European Commission.

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