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Mean-Variance Versus Full-Scale Optimization: In and Out of Sample

MIT Sloan Research Paper No. 4589-05
  • 7
    Citations
  • 8,486
    Usage
  • 18
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    7
    • Citation Indexes
      7
  • Usage
    8,486
    • Abstract Views
      6,391
    • Downloads
      2,095
  • Captures
    18
    • Readers
      18
      • SSRN
        18
  • Ratings
    • Download Rank
      15,165

Paper Description

We present a recent innovation to portfolio construction called full-scale optimization. In contrast to mean-variance analysis, which assumes that returns are normally distributed or that investors have quadratic utility, full-scale optimization identifies the optimal portfolio given any set of return distributions and any description of investor preferences. It therefore yields the truly optimal portfolio in sample, whereas mean-variance analysis provides an approximation to the in-sample truth. Both approaches, however, suffer from estimation error. We employ a bootstrapping procedure to compare the estimation error of full-scale optimization to the combined approximation and estimation error of mean-variance analysis. We find that, to a significant degree, the in-sample superiority of full-scale optimization prevails out-of-sample.

Bibliographic Details

Timothy Adler; Mark Kritzman

Mean-Variance Analysis; Full-Scale Optimization; Portfolio Formation

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