Comparative Evaluation of Modern Deep Learning Methodologies for Portfolio Optimization

Comparative Evaluation of Deep Learning Methodologies for Portfolio Optimization

Problem Formulation and Theoretical Context

The paper addresses the limitations of classical portfolio optimization methodologies—Mean-Variance Optimization (MVO), Capital Asset Pricing Model (CAPM), and Black-Litterman—when exposed to high-dimensional, non-linear, and non-stationary market environments. Static assumptions in covariance matrices and linear feature engineering render these models suboptimal in dynamically shifting regimes, failing to adapt to volatility clustering, fat tails, and abrupt correlation changes. Deep learning architectures can learn hierarchical representations, relational dependencies, and temporal dynamics directly from raw data, with proven success in other domains now increasingly tested in quantitative finance.

The research explores whether hybrid deep learning models, especially those combining temporal and relational modeling (e.g., Transformer+GNN), can deliver robust, interpretable, and institutionally deployable portfolio strategies superior to classical baselines. It also critically examines the conditions—market regimes, architecture sensitivity, feature extraction—under which AI models yield significant improvements.

Deep Learning Architectures for Financial Modeling

The study rigorously benchmarks four modern deep learning frameworks:

• Autoencoders: Unsupervised feature compression, latent factor extraction, denoising, and dimensionality reduction, crucial for robust covariance estimation.
• Graph Neural Networks (GNNs): Explicit modeling of inter-asset dependencies through message passing; generalization to non-linear correlations, sectoral affiliation, and temporal dynamics.
• Deep Reinforcement Learning (DRL): End-to-end learning of dynamic rebalancing and optimal weight adjustment; policy learning via reward maximization calibrated to return and risk metrics.
• Transformers: Sequential modeling of temporal dependencies, effective for forecasting asset returns across multiple macroeconomic and price signals.

Hybrid models—DRL+Autoencoder and Transformer+GNN—integrate feature compression and relational/temporal modeling, leveraging complementary strengths for portfolio construction and risk management.

Methodology, Experimental Design, and Data

Historical daily data (2015–2023) spanning equities, ETFs, and bonds were sourced via Yahoo Finance API. Technical indicators were engineered, with all features standardized for efficient training. Seven strategies were backtested:

• Pure deep learning (DRL, Autoencoder)
• Hybrids (DRL+Autoencoder, Transformer+GNN)
• Classical (MVO, Equal-Weight, 60/40)

Each model was developed with hyperparameter tuning and cost-aware constraints (transaction costs, turnover penalties), subjected to periodic dynamic rebalancing, and evaluated via cumulative and annualized return, Sharpe ratio, volatility, and maximum drawdown—quantifying wealth accumulation, risk, and risk-adjusted performance.

Empirical Results and Comparative Analysis

The empirical results challenge broad claims regarding deep learning dominance and highlight several nuanced findings:

• MVO with accurate predictive inputs achieved the highest cumulative return (461.31%) and Sharpe ratio (1.09), outperforming both deep and hybrid strategies. This contradicts previous literature (e.g., DeMiguel et al., 2009) criticizing MVO for sensitivity and estimation error, suggesting traditional optimization remains competitive when supplied with robust predictions.
• Transformer+GNN exhibited the lowest volatility (14.67%) and drawdown (-18.81%), confirming the significance of relational modeling for risk control and return smoothing.
• Hybrids—Autoencoder+DRL and Transformer+GNN—delivered risk-adjusted returns (Sharpe ratios near 1.04–1.07) exceeding pure DRL (0.37), validating that structural awareness and feature extraction are necessary design elements.
• Standalone DRL underperformed (CAGR 4.62%, maximum drawdown -37.36%), echoing literature findings regarding its lack of robustness in the absence of structure-aware inputs.
• Autoencoder alone mirrored Equal-Weight performance, suggesting dimensionality reduction without active policy learning does not yield enhanced returns.

These findings are consistent with prior studies by Jiang et al. (2017), Zhang et al. (2020), and Feng et al. (2020), reinforcing themes of architecture sensitivity, need for feature extraction, and the practical advantage of relational models.

Implications and Future Research Directions

Practical implications are multi-fold:

• Integration of deep learning models with classical frameworks (MVO) can yield robust, adaptive, and scalable portfolio strategies suitable for institutional contexts.
• Relational and sequential modeling (GNNs and Transformers) are essential for risk-sensitive allocation, capable of reducing inherent volatility and mitigating drawdown in turbulent markets.
• Standalone DRL, even with extensive computational resources, lacks robustness unless augmented with structure-aware designs such as autoencoders.
• Dynamic feature extraction should be leveraged for stability, but must be coupled with active decision-making architectures for meaningful improvements.

Theoretical contributions include the demonstration that optimal portfolio design requires synergy between domain knowledge and data-driven intelligence; over-reliance on either paradigm is suboptimal.

Future work should focus on hybrid frameworks integrating latent representations (Autoencoders, Transformers) within traditional optimization settings, further investigating scalability, generalizability, and interpretability—especially as regulatory and institutional requirements intensify. The exploration of latent financial structures for large-scale asset universes and the adaption of modular architectures for diverse market regimes is warranted.

Conclusion

This comparative evaluation establishes that deep learning methodologies—Autoencoders, GNNs, DRL, Transformers—and hybrid models offer significant enhancements in risk management, stability, and adaptability for portfolio optimization. However, traditional models (MVO), when paired with robust predictive inputs, remain highly competitive and can surpass deep learning approaches in cumulative and risk-adjusted returns. The critical determinant is the integration of informed feature extraction and structure-aware architectures, enabling robustness and superior risk control. A hybridized approach, combining classical theory and modern machine learning, constitutes the optimal strategy for intelligent and scalable portfolio construction (2604.24486).