The Myth of Causal Necessity: Misspecified Models in Mean-Variance Optimization

2025-07-30

AI Summary

This paper challenges the prevailing view that causal modeling is a necessary condition for enhancing investment efficiency, systematically examining whether predictive models—despite structural misspecification (e.g., omitted variables or incorrect functional form)—can still generate valid investment signals and optimize portfolios. Method: Leveraging formal analysis, statistical learning theory, and robust optimization, the study disentangles calibration error from causal deficiency as the primary driver of performance degradation, rigorously distinguishing signal ranking accuracy from magnitude bias. Contribution/Results: Empirical counterexamples demonstrate that non-causal predictive models can yield directionally correct asset rankings, stable mean–variance frontiers, and portfolios with positive Sharpe ratios. The core contribution is establishing a “prediction-first” paradigm: predictive validity—distinct from causal interpretability—suffices to ensure investment effectiveness. This reframes the theoretical and practical foundations of financial machine learning, emphasizing prediction robustness over causal fidelity.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This paper challenges the claim that causal factor modeling is a necessary condition for investment efficiency. Through formal analysis and empirical counterexamples, we show that predictive models, even when structurally misspecified, can produce directionally accurate signals, valid mean-variance frontiers, and positive Sharpe ratios. Contrary to the assertion that causal omission leads to systematic inefficiency, we demonstrate that calibration errors, not lack of causal structure, are the primary source of performance degradation. Drawing from statistical learning theory and robust optimization, we argue that predictive validity, not causal interpretability, is the key metric in portfolio construction. Our theoretical results distinguish between signal ranking and sizing, and our experiments confirm that portfolio optimization remains viable even under omitted variables and nonlinearity. These findings support a predictive-first modeling philosophy: models may be wrong in structure, yet still useful in practice.

Problem

• Challenges necessity of causal models for investment efficiency
• Shows predictive models work despite structural misspecification
• Identifies calibration errors as main performance degradation source

Innovation

• Predictive models enable accurate investment signals
• Calibration errors degrade performance, not causality
• Portfolio optimization works despite model misspecification