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

Author

Listed:
  • Barret Pengyuan Shao

    (Tudor Investment Corporation)

  • John B. Guerard

    (Independent Financial Researcher)

  • Ganlin Xu

    (GuidedChoice.com, Inc.)

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.

Suggested Citation

  • Barret Pengyuan Shao & John B. Guerard & Ganlin Xu, 2025. "Mean-variance and mean-ETL optimizations in portfolio selection: an update," Annals of Operations Research, Springer, vol. 346(1), pages 657-671, March.
    • Handle: RePEc:spr:annopr:v:346:y:2025:i:1:d:10.1007_s10479-024-06337-2
    DOI: 10.1007/s10479-024-06337-2
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    References listed on IDEAS

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