Mean-variance and mean-ETL optimizations in portfolio selection: an update

Barret Pengyuan Shao (), John B. Guerard () and Ganlin Xu ()
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Barret Pengyuan Shao: Tudor Investment Corporation
John B. Guerard: Independent Financial Researcher
Ganlin Xu: GuidedChoice.com, Inc.

Annals of Operations Research, 2025, vol. 346, issue 1, No 32, 657-671

Abstract: Abstract In this research update, we apply the Mean-Variance (MV) and Mean-Expected Tail Loss (ETL) portfolio optimization techniques on earnings forecasting and robust regression-based composite models. A time series model with multivariate normal tempered stable (MNTS) innovations is applied to generate the out-of-sample scenarios for the portfolio optimization. We report that (1) a composite variable of analysts’ forecasts, revisions, and direction of analysts’ revisions continues to produce value in portfolio construction; (2) robust regression-based models continue to produce meaningful active returns; and (3) the Mean-Variance and Mean-ETL portfolio optimizations produce statistically significant active returns, passing the Markowitz and Xu (Journal of Portfolio Management 21:1–60, 1994) data mining corrections test.

Keywords: Markowitz portfolio selection; Mean-variance analysis; Mean-expected tail loss (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-024-06337-2

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