Mean-Variance Versus Full-Scale Optimization: In and Out of Sample
MIT Sloan Research Paper No. 4589-05
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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.
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