Causal Portfolio Optimization: Principles and Sensitivity-Based Solutions

ArXiv ID: 2504.05743 “View on arXiv”

Authors: Unknown

Abstract

Fundamental and necessary principles for achieving efficient portfolio optimization based on asset and diversification dynamics are presented. The Commonality Principle is a necessary and sufficient condition for identifying optimal drivers of a portfolio in terms of its diversification dynamics. The proof relies on the Reichenbach Common Cause Principle, along with the fact that the sensitivities of portfolio constituents with respect to the common causal drivers are themselves causal. A conformal map preserves idiosyncratic diversification from the unconditional setting while optimizing systematic diversification on an embedded space of these sensitivities. Causal methodologies for combinatorial driver selection are presented, such as the use of Bayesian networks and correlation-based algorithms from Reichenbach’s principle. Limitations of linear models in capturing causality are discussed, and included for completeness alongside more advanced models such as neural networks. Portfolio optimization methods are presented that map risk from the sensitivity space to other risk measures of interest. Finally, the work introduces a novel risk management framework based on Common Causal Manifolds, including both theoretical development and experimental validation. The sensitivity space is predicted along the common causal manifold, which is modeled as a causal time system. Sensitivities are forecasted using SDEs calibrated to data previously extracted from neural networks to move along the manifold via its tangent bundles. An optimization method is then proposed that accumulates information across future predicted tangent bundles on the common causal time system manifold. It aggregates sensitivity-based distance metrics along the trajectory to build a comprehensive sensitivity distance matrix. This matrix enables trajectory-wide optimal diversification, taking into account future dynamics.

Keywords: Portfolio Optimization, Causal Inference, Bayesian Networks, SDE Forecasting, Diversification Dynamics, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper heavily relies on advanced mathematical concepts like manifolds, SDEs, and causal geometry, indicating high math complexity. While it mentions experimental validation, the excerpt focuses on theoretical frameworks, proofs, and algorithms without providing specific backtest results, datasets, or code, placing it closer to theoretical ‘Lab Rats’ than empirical ‘Holy Grail’.

  flowchart TD
    Goal["Research Goal<br>Efficient Portfolio Optimization<br>via Causal Dynamics"]
    
    Input["Input Data<br>Asset Returns & Sensitivities"]
    
    Method["Key Methodology<br>Commonality Principle &<br>Bayesian Networks"]
    
    Process["Computational Process<br>Embedding Sensitivity Space<br>via Conformal Map"]
    
    Forecast["Forecasting<br>SDEs on Common Causal<br>Manifold (Tangent Bundles)"]
    
    Optimize["Optimization<br>Trajectory-wide Diversification<br>via Distance Matrix"]
    
    Outcome["Key Outcomes<br>Common Causal Manifolds &<br>Risk Management Framework"]

    Goal --> Input
    Input --> Method
    Method --> Process
    Process --> Forecast
    Forecast --> Optimize
    Optimize --> Outcome