Paper: arXiv 2511.17963

Authors: Jun Kevin, Pujianto Yugopuspito

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.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a sophisticated hybrid model combining LSTM and PPO with multiple derivations and complex loss functions, while also detailing a multi-asset dataset, specific backtesting metrics, and transaction cost adjustments, indicating strong empirical implementation.

Research Flowchart

  flowchart TD
  A["Research Goal: Dynamic Portfolio Optimization"] --> B["Hybrid LSTM-PPO Framework"]
  
  B --> C["Data Input<br>Multi-Asset (Jan 2018 - Dec 2024)<br>US/Indonesia Equities, Treasuries, Crypto"]
  
  C --> D["Process: LSTM Forecasting<br>Deep Recurrent Networks<br>Temporal Trend Prediction"]
  
  C --> E["Process: PPO Agent<br>Proximal Policy Optimization<br>Continuous Action Space Allocation"]
  
  D --> F["Fusion & Adaptation<br>Dynamic Rebalancing + Transaction Cost Adjustment"]
  E --> F
  
  F --> G["Benchmarking<br>vs. Equal-weight, Index, LSTM-only, PPO-only"]
  
  G --> H["Key Outcomes:<br>↑ Higher Annualized Returns<br>↑ Sharpe Ratio<br>↓ Max Drawdown<br>↑ Market Regime Resilience"]

Cited References (30)

  1. Forecasting cryptocurrency returns using classical statistical and deep learning techniques (2024) — link
  2. Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods (2024) — link
  3. Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework (2024)
  4. Bitcoin replication using machine learning (2024) — link
  5. A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin — → 2407.18334
  6. Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies (2023) — link
  7. Machine learning for cryptocurrency market prediction and trading (2022) — link
  8. Deep Learning Forecasting in Cryptocurrency High-Frequency Trading (2021) — link
and 22 more
  1. A Contrastive Deep Learning Approach to Cryptocurrency Portfolio with US Treasuries (2024) — link
  2. Predictive crypto-asset automated market maker architecture for decentralized finance using deep reinforcement learning (2024) — link
  3. Dynamic portfolio choice with uncertain rare-events risk in stock and cryptocurrency markets (2023) — link
  4. Using Deep Reinforcement Learning with Hierarchical Risk Parity for Portfolio Optimization (2023) — link
  5. Deep Learning for High-Frequency Cryptocurrency Trend Detection Incorporating Technical Indicators and a New Approach For Data Stationarity (2024) — link
  6. Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making (2020)
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  8. Deep graph convolutional reinforcement learning for financial portfolio management – DeepPocket (2021) — link
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  14. Maximizing portfolio profitability during a cryptocurrency downtrend: A Bitcoin Blockchain transaction-based approach (2023)
  15. Top 100 Cryptocurrencies by Market Capitalization (2025) — link
  16. Portfolio Optimization through Hybrid Deep Learning and Genetic Algorithms: Vine Copula–GARCH–EVT–CVaR Model (2023) — link
  17. Reinforcement-Learning-Based Portfolio Management with Augmented Asset Movement Prediction States (2020) — link
  18. Multi-Period Portfolio Optimization Using a Deep Reinforcement Learning Hyper-Heuristic Approach (2023) — link
  19. Outperforming the Tutor: Expert-Infused Deep Reinforcement Learning for Dynamic Portfolio Selection of Diverse Assets (2024) — link
  20. A Deep Learning Framework for Financial Time Series Using Stacked Autoencoders and Long Short-Term Memory (2017) — link
  21. Decision-Making for Financial Trading: A Fusion Approach of Machine Learning and Portfolio Optimization (2019) — link
  22. A Hybrid Deep Learning Model for Stock Index Prediction Using LSTM and GARCH (2020) — link

1 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.