Universal Portfolio Shrinkage

Bryan Kelly, Semyon Malamud, Mohammad Pourmohammadi and Fabio Trojani
Additional contact information
Bryan Kelly: Yale School of Management
Semyon Malamud: Ecole Polytechnique Fédérale de Lausanne, Swiss Finance Institute, and CEPR
Fabio Trojani: University of Geneva, University of Turin and Swiss Finance Institute

No 23-119, Swiss Finance Institute Research Paper Series from 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.

Pages: 63 pages
Date: 2023-12
New Economics Papers: this item is included in nep-ifn
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Related works:
Working Paper: Universal Portfolio Shrinkage (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp23119

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