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The Markowitz mean-variance optimization procedure is highly appreciated as a theoretical result in literature.However,it has been demonstrated to be less applicable in practice.The portfolio formed by using the classical Mean-Variance approach always resultsin extreme portfolio weights that fluctuate substantially over time and perform poorly in the out-of-sample forecasting.The reason for this problem is due to the substantial estimation error of the inputs of the optimization procedure.The classical mean-variance approach which uses the sample mean and sample covariance matrix as inputs always results in serious errors.Applying large dimensional data analysis,we prove that the plug-in return is larger than the theoretical optimal return when the dimension of the population goes to infinity with same order of the sample number.This phenomenon is called “over-prediction” by Bai,Liu,Wong (2009) in which they advise a bootstrap -corrected estimation to improve the plug-in estimation in the optimal return estimation.But compared with the plug-in estimation,the performance of the bootstrap-corrected estimation is not satisfied in the optimal allocation and the corresponding risk.That is because in the bootstrap-corrected estimation they still use the sample covariance matrix as the estimation of the population covariance.