In this paper, we build efficient portfolios using different frameworks proposed in the literature and drawing upon several datasets that contain an increasing number of predictors as conditioning information. We carry an extensive empirical study to investigate approaches that impose sparsity and dimensionality reduction, as well as possible latent factors driving the returns of the risky assets. In contrast to previous studies that made use of naive OLS and low-dimension information sets, we find that (i) accounting for large conditioning information sets, and (ii) the use of variable selection, shrinkage methods and factor models, such as the principal component regression and the partial least squares, provides better out-of-sample results as measured by Sharpe ratios, implied Sharpe ratios, and higher certainty equivalent returns (CER).

在本文中，我们使用文献中提出的不同框架构建有效的投资组合，并利用包含越来越多的预测变量作为条件信息的几个数据集。我们进行了广泛的实证研究，以调查施加稀疏和降维的方法，以及驱动风险资产回报的可能潜在因素。与之前使用朴素 OLS 和低维信息集的研究相比，我们发现 (i) 考虑了大型条件信息集，以及 (ii) 使用变量选择、收缩方法和因子模型，例如主成分回归和偏最小二乘法提供了更好的样本外结果，如夏普比率、隐含夏普比率和更高的确定性等效回报 (CER)。