Hybrid LSTM and PPO Networks for Dynamic Portfolio Optimization

TL;DR

This paper presents a hybrid deep learning framework combining LSTM forecasting and PPO reinforcement learning to optimize portfolios dynamically, demonstrating improved performance across diverse financial assets and market conditions.

Contribution

The paper introduces a novel hybrid LSTM-PPO model that integrates time-series forecasting with adaptive reinforcement learning for portfolio management, outperforming traditional methods.

Findings

Higher annualized returns compared to baselines

Greater resilience in volatile market regimes

Improved risk-adjusted performance metrics

Abstract

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.