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The Black-Litterman model extends the framework of the Markowitz ModernPortfolio Theory to incorporate investor views. We consider a case wheremultiple view estimates, including uncertainties, are given for the sameunderlying subset of assets at a point in time. This motivates ourconsideration of data fusion techniques for combining information from multiplesources. In particular, we consider consistency-based methods that yield fusedview and uncertainty pairs; such methods are not common to the quantitativefinance literature. We show a relevant, modern case of incorporating machinelearning model-derived view and uncertainty estimates, and the impact onportfolio allocation, with an example subsuming Arbitrage Pricing Theory. Hencewe show the value of the Black-Litterman model in combination with informationfusion and artificial intelligence-grounded prediction methods.

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