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.

中文翻译：

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使用自举神经网络构建投资组合：来自全球股市的证据

该研究通过组合自举构建方法和自举神经网络架构研究了全球股票市场的投资价值。自举样本程序将生成一个残留样本，然后将其合并到预期收益和协变量矩阵的估计中。输出由传统的Markowitz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Markowitz均方差分析，James-Stein和最小方差估计量进行了比较。从实证结果来看，它表明这种新颖的方法明显优于大多数基于各种风险调整后的绩效指标的基准模型。