A Geometric Approach To Asset Allocation With Investor Views

TL;DR

This paper introduces a geometric method using generalized Wasserstein barycenter to incorporate investor views into portfolio optimization, offering greater flexibility and improved decision rewards over traditional models.

Contribution

The paper presents a novel geometric approach leveraging GWB to integrate investor views into asset allocation, enhancing flexibility and decision rewards compared to Black-Litterman.

Findings

The geometric approach outperforms Black-Litterman in flexibility and reward metrics.

Empirical and theoretical evidence supports the advantages of the proposed method.

Quantitative comparisons demonstrate improved portfolio estimates.

Abstract

In this article, a geometric approach to incorporating investor views in portfolio construction is presented. In particular, the proposed approach utilizes the notion of generalized Wasserstein barycenter (GWB) to combine the statistical information about asset returns with investor views to obtain an updated estimate of the asset drifts and covariance, which are then fed into a mean-variance optimizer as inputs. Quantitative comparisons of the proposed geometric approach with the conventional Black-Litterman model (and a closely related variant) are presented. The proposed geometric approach provides investors with more flexibility in specifying their confidence in their views than conventional Black-Litterman model-based approaches. The geometric approach also rewards the investors more for making correct decisions than conventional BL based approaches. We provide empirical and theoretical justifications for our claim.