Enhancing Portfolio Optimization with Deep Learning Insights

ArXiv ID: 2601.07942 “View on arXiv”

Authors: Brandon Luo, Jim Skufca

Abstract

Our work focuses on deep learning (DL) portfolio optimization, tackling challenges in long-only, multi-asset strategies across market cycles. We propose training models with limited regime data using pre-training techniques and leveraging transformer architectures for state variable inclusion. Evaluating our approach against traditional methods shows promising results, demonstrating our models’ resilience in volatile markets. These findings emphasize the evolving landscape of DL-driven portfolio optimization, stressing the need for adaptive strategies to navigate dynamic market conditions and improve predictive accuracy.

Keywords: Deep Learning, Transformers, Portfolio Optimization, Pre-training, Regime Adaptation, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (LSTMs, transformers) and optimization techniques (gradient ascent, Sharpe ratio loss), representing high mathematical complexity. It demonstrates strong empirical rigor with specific backtesting protocols, statistical tests (Mann-Whitney U), and robustness experiments across extended timeframes and asset compositions.

  flowchart TD
    R["Research Goal: Enhancing Portfolio Optimization with DL"] --> I["Data & Inputs"]
    
    subgraph I ["Inputs"]
        I1["Multi-Asset Market Data"]
        I2["Limited Regime Data"]
        I3["State Variables"]
    end
    
    I --> M["Methodology"]
    
    subgraph M ["Key Methodology"]
        M1["Pre-training on Limited Data"]
        M2["Transformer Architecture Integration"]
        M3["Regime Adaptation Training"]
    end
    
    M --> C["Computational Process"]
    
    subgraph C ["Process"]
        C1["Model Training"]
        C2["Long-Only Strategy Generation"]
        C3["Multi-Asset Portfolio Allocation"]
    end
    
    C --> F["Key Findings & Outcomes"]
    
    subgraph F ["Outcomes"]
        F1["Promising Results vs. Traditional Methods"]
        F2["Resilience in Volatile Markets"]
        F3["Adaptive Strategies for Dynamic Conditions"]
    end
    
    F --> End["Conclusions: Evolving DL-Driven Optimization Landscape"]