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Universal Portfolio Shrinkage

Author

Listed:
  • Bryan Kelly

    (Yale School of Management)

  • Semyon Malamud

    (Ecole Polytechnique Fédérale de Lausanne, Swiss Finance Institute, and CEPR)

  • Mohammad Pourmohammadi

    (University of Geneva and Swiss Finance Institute)

  • Fabio Trojani

    (University of Geneva, University of Turin and Swiss Finance Institute)

Abstract

We introduce a novel shrinkage methodology for building optimal portfolios in environments of high complexity where the number of assets is comparable to or larger than the number of observations. Our universal portfolio shrinkage approximator(UPSA) is derived in closed form, is easy to implement, and dominates other existing shrinkage methods. It exhibits an explicit two-fund separation, optimally combining Markowitz with a complexity correction. Instead of annihilating the low-variance principal components, UPSA weights them efficiently. Contrary to conventional wisdom, low in-sample variance principal components (PCs) are key to out-of-sample model performance. By optimally incorporating them into portfolio construction, UPSA produces a stochastic discount factor that significantly dominates its PC-sparse counterparts. Thus, PC-sparsity is just an artifact of inefficient shrinkage.

Suggested Citation

  • Bryan Kelly & Semyon Malamud & Mohammad Pourmohammadi & Fabio Trojani, 2023. "Universal Portfolio Shrinkage," Swiss Finance Institute Research Paper Series 23-119, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp23119
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