Designing a portfolio that meets expectations is notoriously difficult. Optimised portfolios typically fail to deliver average performances or risk realisations aligned with ex-ante expected values. A new Review of Financial Studies paper shows that the forecasting errors attributed to this worse-than-expected performance are themselves predictable. And so, using past forecasting errors to calibrate mean-variance inputs generates portfolio performance that meets expectations.
The authors call this process the ‘Galton’ correction. Relative to other mean-variance portfolio optimisation methods, a Galton-optimised portfolio has better Sharpe Ratios, smaller risk forecast errors and more attractive performance after transaction costs. The correction is also applicable in a machine-learning and AI context to help nonlinear portfolio optimisation, and the authors suggest it is more robust in a real-world context than other methods.
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