View fusion vis-\`a-vis a Bayesian interpretation of Black-Litterman for portfolio allocation

TL;DR

This paper explores integrating multiple, uncertain views from machine learning models into the Black-Litterman portfolio framework using data fusion techniques, enhancing asset allocation strategies.

Contribution

It introduces a novel approach combining data fusion with Black-Litterman, incorporating multiple sources of view estimates and uncertainties, including AI-derived predictions.

Findings

Fusion-based views improve portfolio diversification.

Incorporating AI predictions enhances allocation accuracy.

Method demonstrates effectiveness with Arbitrage Pricing Theory.

Abstract

The Black-Litterman model extends the framework of the Markowitz Modern Portfolio Theory to incorporate investor views. We consider a case where multiple view estimates, including uncertainties, are given for the same underlying subset of assets at a point in time. This motivates our consideration of data fusion techniques for combining information from multiple sources. In particular, we consider consistency-based methods that yield fused view and uncertainty pairs; such methods are not common to the quantitative finance literature. We show a relevant, modern case of incorporating machine learning model-derived view and uncertainty estimates, and the impact on portfolio allocation, with an example subsuming Arbitrage Pricing Theory. Hence we show the value of the Black-Litterman model in combination with information fusion and artificial intelligence-grounded prediction methods.