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Pandemic-proofing Out-of-sample Portfolio Evaluations

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  • Hrishikesh Vinod

    (Fordham University, Department of Economics)

Abstract

Evaluation of the performance of portfolios and of various methods of ranking them has to be out-of-sample. Otherwise, selection methods that fit the past data best would always win. Suppose the time series chosen for out-of-sample evaluation happens to have any (upward, downward, zigzag) trend. In that case, portfolio selec- tion methods for that trend will work best but fail in general. We describe algorithms for the removal of such bias by using randomization. The R package 'generalCorr' has them. We use 169-month Dow Jones stock data to illustrate outOFsamp(), outOFsell().

Suggested Citation

  • Hrishikesh Vinod, 2023. "Pandemic-proofing Out-of-sample Portfolio Evaluations," Fordham Economics Discussion Paper Series dp2023-04er:dp2023-04, Fordham University, Department of Economics.
    • Handle: RePEc:frd:wpaper:dp2023-04er:dp2023-04
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    More about this item

    Keywords

    Portfolio choice;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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