Portfolio selection revisited

Alex Shkolnik (), Alec Kercheval (), Hubeyb Gurdogan (), Lisa R. Goldberg () and Haim Bar ()
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Alex Shkolnik: University of California
Alec Kercheval: Florida State University
Hubeyb Gurdogan: University of California
Lisa R. Goldberg: University of California
Haim Bar: University of Connecticut

Annals of Operations Research, 2025, vol. 346, issue 1, No 12, 137-155

Abstract: Abstract In 1952, Harry Markowitz formulated portfolio selection as a trade-off between expected, or mean, return and variance. This launched a massive research effort devoted to finding suitable inputs to mean-variance optimization. The estimation problem is high dimensional and a factor model is at the core of many attempts. A factor model can reduce the number of parameters that need to be estimated to a manageable size, but these parameters may incorporate substantial, hidden estimation error. Recent analysis elucidates the nature of this error, identifies a mechanism by which it can corrupt optimization and provides a method for its mitigation. We explore this analysis here by illustrating how to improve the volatility ratio of large optimized portfolios, leading to superior portfolio selection. $$^{*}$$ ∗

Keywords: Mean-variance portfolio optimization; High dimensional covariance estimation; Principal component analysis; Factor models; Volatility ratio; James-Stein estimation; Shrinkage (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-024-06340-7

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