Portfolio efficiency with high-dimensional data as conditioning information

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).

Automated summary

By utilizing various frameworks and datasets with increasing predictors, efficient portfolios can be built effectively. Compared to previous studies using naive OLS and low-dimensional information sets, better out-of-sample results can be achieved by considering large conditioning information sets and using methods like variable selection, shrinkage methods, and factor models.