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ESTIMATION AND SELECTION BIAS IN MEAN‐VARIANCE PORTFOLIO SELECTION
Author(s) -
Frankfurter George M.,
Lamoureux Christopher G.
Publication year - 1989
Publication title -
journal of financial research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.319
H-Index - 49
eISSN - 1475-6803
pISSN - 0270-2592
DOI - 10.1111/j.1475-6803.1989.tb00111.x
Subject(s) - portfolio , estimator , econometrics , selection (genetic algorithm) , context (archaeology) , parametric statistics , monte carlo method , estimation , variance (accounting) , selection bias , statistics , computer science , economics , mathematics , financial economics , machine learning , management , accounting , paleontology , biology
Much research has focused on the problem of selecting portfolios without the benefit of parametric measures of risk and return. In this paper, a Monte Carlo technique is used to isolate the extent and nature of the problems introduced by this practice. The technique is employed in the context of classical statistical methodology without permitting short sales. It is shown that using estimators of expected return and risk not only obscures parametric values, but also affects portfolio composition in the Markowitz framework. In this study, these two components of bias are isolated and measured.