Deep Declarative Risk Budgeting Portfolios

ArXiv ID: 2504.19980 “View on arXiv”

Authors: Manuel Parra-Diaz, Carlos Castro-Iragorri

Abstract

Recent advances in deep learning have spurred the development of end-to-end frameworks for portfolio optimization that utilize implicit layers. However, many such implementations are highly sensitive to neural network initialization, undermining performance consistency. This research introduces a robust end-to-end framework tailored for risk budgeting portfolios that effectively reduces sensitivity to initialization. Importantly, this enhanced stability does not compromise portfolio performance, as our framework consistently outperforms the risk parity benchmark.

Keywords: end-to-end deep learning, implicit layers, risk budgeting, risk parity, optimization stability, Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced implicit layers, bounded softmax constraints, and differential optimization, indicating high mathematical sophistication, while the methodology is validated with real market data, Sharpe ratios, and out-of-sample backtesting demonstrating robust empirical rigor.

  flowchart TD
    A["Research Goal: Robust End-to-End Portfolio Optimization"] --> B["Data: Asset Returns for Risk Budgeting"]
    B --> C["Methodology: Deep Declarative Framework w/ Implicit Layers"]
    C --> D["Problem: Sensitivity to Neural Initialization"]
    D --> E["Solution: Proposed Stability-Enhancing Training"]
    E --> F["Computational Process: End-to-End Optimization"]
    F --> G["Outcome 1: Reduced Sensitivity to Initialization"]
    F --> H["Outcome 2: Outperforms Risk Parity Benchmark"]