Paper: arXiv 2301.13594

Authors: Trent Spears, Stefan Zohren, Stephen Roberts

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a novel synthesis of data fusion methods and Black-Litterman portfolio optimization, specifically demonstrating how to aggregate multiple machine learning-derived view estimates. The mathematical framework for consistency-based view fusion is well-constructed, though the empirical validation relies largely on illustrative and APT-grounded synthetic setups rather than exhaustive historical backtesting.

Research Flowchart

  flowchart TD
    A[Goal: Integrate Multiple Machine Learning Views into Black-Litterman Model] --> B[Data Inputs: Multi-Asset Equities & Arbitrage Pricing Theory Factor Views]
    B --> C[Consistency-Based Data Fusion]
    C --> D[Fused View Vector & Fused Uncertainty Matrix]
    D --> E[Black-Litterman Bayesian Optimization Process]
    E --> F[Outcomes: Robust Portfolio Allocation & Enhanced Return Estimates]