Despite having become firmly established as one of the major cornerstone principles of modern finance, traditional Markowitz mean-variance analysis has, nevertheless, failed to gain widespread acceptance as a practical tool for equity management. The Markowitz optimization enigma essentially centers on the severe estimation risk associated with the input parameters, as well as the resultant financially irrelevant or even false optimal portfolios and asset allocation proposals. We therefore propose a portfolio construction method in the present study which incorporates the adoption of bootstrapping neural network architecture. In specific terms, a residual bootstrapping sample, which is derived
from multilayer feedforward neural networks, is incorporated into the estimation of the expected returns and the covariance matrix, which are then, in turn, integrated into the traditional Markowitz optimization procedure. The efficacy of our proposed approach is illustrated by comparing it with traditional Markowitz mean-variance analysis, as well as
the James-Stein and minimum-variance estimators, with the empirical results indicating that this novel approach significantly outperforms the benchmark models, in terms of various risk-adjusted performance measures. The evidence provided in this study suggests that this new approach has significant promise with regard to the enhancement of the
investment value of Markowitz mean-variance analysis.