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Portfolio Selection with Contrarian Strategy

Author

Listed:
  • Zhichao Lu

    (Soochow University)

  • Peiyuan Pang

    (Soochow University)

  • Yuhong Xu

    (Soochow University)

  • Wenxin Zhang

    (Soochow University)

Abstract

Compared with the extensive empirical literature on contrarian strategy, we develop a dynamic mean-variance model with geometric value-reversion asset prices, which implies a contrarian strategy. The model is solved (semi) explicitly under three asset price evaluations: constant valuation, exponential-varying valuation, and geometric average valuation. From a mathematical perspective, it is nontrivial to solve the extended HJB equations under stochastic opportunities. We demonstrate that our strategy exhibits the same monotonicity as that of the traditional constant relative risk-averse utility, and the welfare loss of using the dynamic mean-variance criterion is rather small, supporting that our model is a good approximation to the constant relative risk-averse utility. Empirical tests show that our strategies can help an investor achieve a less volatile wealth trajectory.

Suggested Citation

  • Zhichao Lu & Peiyuan Pang & Yuhong Xu & Wenxin Zhang, 2024. "Portfolio Selection with Contrarian Strategy," Methodology and Computing in Applied Probability, Springer, vol. 26(2), pages 1-28, June.
    • Handle: RePEc:spr:metcap:v:26:y:2024:i:2:d:10.1007_s11009-024-10085-y
    DOI: 10.1007/s11009-024-10085-y
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Portfolio selection; Contrarian strategy; Geometric mean-reversion process; Mean variance;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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