This study proposes a portfolio optimization framework that integrates advanced deep learning architectures with traditional financial models to enhance risk-adjusted performance. Using historical data from 2015-2023 across equities, ETFs, and bonds, the research evaluates the predictive power of Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), Transformers, and Autoencoders. The models jointly address covariance estimation, return forecasting, dynamic asset allocation, and dimensionality reduction. Hybrid approaches such as Transformer+GNN and Autoencoder+DRL are also explored to capture both relational and temporal market structures. Performance is assessed through backtesting using metrics including volatility, cumulative return, maximum drawdown, annualized return, and Sharpe ratio across seven strategies, including Equal-Weighted, 60/40 allocation, and Mean-Variance Optimization (MVO). Results show that hybrid models provide superior stability and risk control, with Transformer+GNN achieving the lowest volatility and drawdown. MVO, when paired with well-calibrated inputs, delivers the highest cumulative return and Sharpe ratio, highlighting the continued relevance of traditional methods. Standalone DRL underperforms due to limited structural awareness, while Autoencoders exhibit behavior similar to Equal-Weight strategies, emphasizing the need for dynamic policy learning. These findings align with existing literature on relational modeling and feature compression in finance. Overall, the study demonstrates that combining deep learning with financial theory yields robust and adaptive portfolio strategies and suggests exploring latent representations within traditional optimization frameworks to improve scalability and performance.

翻译：本研究提出一个整合先进深度学习架构与传统金融模型的投资组合优化框架，旨在提升风险调整后收益。基于2015-2023年间股票、ETF和债券的历史数据，本项研究评估了图神经网络（GNNs）、深度强化学习（DRL）、Transformer和自编码器（Autoencoders）的预测能力。这些模型协同处理协方差估计、收益预测、动态资产配置和降维问题。研究还探索了Transformer+GNN和Autoencoder+DRL等混合方法，以捕捉市场中的关系结构和时序结构。通过回测，使用波动率、累积收益、最大回撤、年化收益和夏普比率等指标，对包括等权重、60/40配置和均值-方差优化（MVO）在内的七种策略进行评估。结果表明，混合模型在稳定性和风险控制方面表现更优，其中Transformer+GNN实现了最低的波动率和最大回撤。当MVO与良好校准的输入相结合时，能获得最高的累积收益和夏普比率，凸显了传统方法的持续相关性。独立DRL因结构感知能力有限而表现不佳，自编码器则表现出与等权重策略相似的特性，强调了动态策略学习的必要性。这些发现与现有关于金融关系建模和特征压缩的文献一致。总体而言，该研究证明了深度学习与金融理论结合能产生稳健且适应性强的投资组合策略，并建议在传统优化框架中探索潜在表征以提升可扩展性和性能。