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arXiv · 2301.13594

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

Trent Spears·Stefan Zohren·Stephen Roberts
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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.

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Trent Spears, Stefan Zohren, Stephen Roberts. 2023-01-31. View fusion vis-\`a-vis a Bayesian interpretation of Black-Litterman for portfolio allocation. https://arxiv.org/abs/2301.13594

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