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

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• Trent Spears (Oxford)
• Tuesday 13 December 2022, 15:00-16:00
• 310 Watson.

If you have a question about this talk, please contact Jamie Alcock.

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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 Ross’s Arbitrage Pricing Theory.

This talk is part of the Quantitative Methods in Finance seminar series.

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