The study investigates the investment value of global stock markets by a portfolio construction method combined with bootstrapping neural network architecture. A residual sample will be generated from bootstrapping sample procedure and then incorporated into the estimation of the expected returns and the covariant matrix. The outputs are further processed by the traditional Markowitz optimization procedure. In order to examine the efficacy of the proposed approach, the illustrated case was compared with traditional Markowitz mean–variance analysis, as well as the James–Stein and minimum-variance estimators. From the empirical results, it indicated that this novel approach significantly outperforms most of benchmark models based on various risk-adjusted performance measures. It can be shown that this new approach has great promise for enhancing the estimation of the investment value by Markowitz mean–variance analysis in the global stock markets.

该研究通过结合自举神经网络架构的投资组合构建方法来调查全球股票市场的投资价值。残差样本将从自举样本过程中生成，然后纳入预期收益和协变矩阵的估计中。输出由传统的马科维茨优化程序进一步处理。为了检验所提出方法的有效性，将图示案例与传统的马科维茨均值方差分析以及詹姆斯斯坦和最小方差估计量进行了比较。实证结果表明，这种新颖的方法显着优于大多数基于各种风险调整绩效指标的基准模型。可以看出，这种新方法对于提高马科维茨均值方差分析对全球股票市场投资价值的估计具有很大的前景。