Hybrid LSTM and PPO Networks for Dynamic Portfolio Optimization
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Abstract (arXiv)
This paper introduces a hybrid framework for portfolio optimization that fuses Long Short-Term Memory (LSTM) forecasting with a Proximal Policy Optimization (PPO) reinforcement learning strategy. The proposed system leverages the predictive power of deep recurrent networks to capture temporal dependencies, while the PPO agent adaptively refines portfolio allocations in continuous action spaces, allowing the system to anticipate trends while adjusting dynamically to market shifts. Using multi-asset datasets covering U.S. and Indonesian equities, U.S. Treasuries, and major cryptocurrencies from January 2018 to December 2024, the model is evaluated against several baselines, including equal-weight, index-style, and single-model variants (LSTM-only and PPO-only). The framework's performance is benchmarked against equal-weighted, index-based, and single-model approaches (LSTM-only and PPO-only) using annualized return, volatility, Sharpe ratio, and maximum drawdown metrics, each adjusted for transaction costs. The results indicate that the hybrid architecture delivers higher returns and stronger resilience under non-stationary market regimes, suggesting its promise as a robust, AI-driven framework for dynamic portfolio optimization.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 30 references that are in the map. First posted on arXiv 22 Nov 2025.
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3 of 30 references.
References · 3
- 2020Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction Statescited by 55
- 2021Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocketcited by 13
- 2024A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoincited by 1
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