Multi-Agent Regime-Conditioned Diffusion (MARCD) for CVaR-Constrained Portfolio Decisions

ArXiv ID: 2510.10807 “View on arXiv”

Authors: Ali Atiah Alzahrani

Abstract

We examine whether regime-conditioned generative scenarios combined with a convex CVaR allocator improve portfolio decisions under regime shifts. We present MARCD, a generative-to-decision framework with: (i) a Gaussian HMM to infer latent regimes; (ii) a diffusion generator that produces regime-conditioned scenarios; (iii) signal extraction via blended, shrunk moments; and (iv) a governed CVaR epigraph quadratic program. Contributions: Within the Scenario stage we introduce a tail-weighted diffusion objective that up-weights low-quantile outcomes relevant for drawdowns and a regime-expert (MoE) denoiser whose gate increases with crisis posteriors; both are evaluated end-to-end through the allocator. Under strict walk-forward on liquid multi-asset ETFs (2005-2025), MARCD exhibits stronger scenario calibration and materially smaller drawdowns: MaxDD 9.3% versus 14.1% for BL (a 34% reduction) over 2020-2025 out-of-sample. The framework provides an auditable pipeline with explicit budget, box, and turnover constraints, demonstrating the value of decision-aware generative modeling in finance.

Keywords: Gaussian HMM, Diffusion models, Conditional Value at Risk (CVaR), Regime shifts, Portfolio optimization, Multi-asset (ETFs)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including diffusion models, Gaussian HMMs, mixture-of-experts denoisers, and convex optimization with CVaR epigraph QP, supported by theoretical theorems and proofs. Empirically, it demonstrates rigorous walk-forward backtesting on real ETF data (2005-2025) with explicit constraints, transaction costs, and detailed out-of-sample metrics, showing a 34% reduction in max drawdown.

  flowchart TD
    A["Research Goal: Assess Regime-Conditioned Generative Models for CVaR-Constrained Portfolios under Shifts"] --> B["Data: Liquid Multi-Asset ETFs (2005-2025)"]

    subgraph C ["MARCD Methodology"]
        C1["Gaussian HMM<br>Latent Regime Inference"]
        C2["Diffusion Generator<br>Tail-Weighted &<br>Regime-Conditioned Scenarios"]
        C3["Signal Extraction<br>Blended & Shrunk Moments"]
        C4["CVaR Epigraph QP<br>Explicit Constraints"]
    end

    B --> C1
    C1 --> C2
    C2 --> C3
    C3 --> C4

    C4 --> D["Walk-Forward Validation<br>Out-of-Sample: 2020-2025"]

    D --> E["Key Findings"]
    E --> E1["Superior Scenario Calibration"]
    E --> E2["Max Drawdown: 9.3% (vs. 14.1% for BL)"]
    E --> E3["34% Reduction in Max Drawdown"]
    E --> E4["Validated Decision-Aware Generative Modeling"]