Paper: arXiv 2511.11481

Authors: Emmanuel Lwele, Sabuni Emmanuel, Sitali Gabriel Sitali

Abstract

This paper presents a deep reinforcement learning (DRL) framework for dynamic portfolio optimization under market uncertainty and risk. The proposed model integrates a Sharpe ratio-based reward function with direct risk control mechanisms, including maximum drawdown and volatility constraints. Proximal Policy Optimization (PPO) is employed to learn adaptive asset allocation strategies over historical financial time series. Model performance is benchmarked against mean-variance and equal-weight portfolio strategies using backtesting on high-performing equities. Results indicate that the DRL agent stabilizes volatility successfully but suffers from degraded risk-adjusted returns due to over-conservative policy convergence, highlighting the challenge of balancing exploration, return maximization, and risk mitigation. The study underscores the need for improved reward shaping and hybrid risk-aware strategies to enhance the practical deployment of DRL-based portfolio allocation models.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematics including gradient ascent derivations for Sharpe ratio optimization and constraints via softmax, while demonstrating empirical rigor with backtesting on historical data against benchmarks and risk metrics like maximum drawdown.

Research Flowchart

  flowchart TD
  A["Research Goal: DRL for Dynamic Portfolio Optimization"] --> B["Methodology: PPO with Sharpe Reward & Risk Constraints"]
  B --> C["Data: Historical High-Performing Equities"]
  C --> D{"Computational Process"}
  D --> E["Training Agent"]
  D --> F["Backtesting"]
  E --> G["Over-conservative Policy Convergence"]
  F --> H["Benchmark vs. Mean-Variance & Equal-Weight"]
  G & H --> I["Outcomes"]
  I --> J["Stable Volatility"]
  I --> K["Degraded Risk-Adjusted Returns"]
  I --> L["Need for Improved Reward Shaping"]

Cited References (26)

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  2. Utilising digital media as a second language (L2) support: A case study on Netflix with translation applications (2020)
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  4. Stock trading with recurrent reinforcement learning (RRL) (2016)
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  6. Deep reinforcement learning for optimal portfolio allocation: A comparative study with mean-variance optimization (2023)
  7. A Bibliometric Analysis of Artificial Intelligence Technique in Financial Market (2021) — link
  8. Multi-agent deep reinforcement learning algorithm with trend consistency regularization for portfolio management (2023)
and 18 more
  1. Performance functions and reinforcement learning for trading systems and portfolios (1998)
  2. Stock market prediction using machine learning classifiers and social media, news (2022)
  3. An efficient word embedding and deep learning based model to forecast the direction of stock exchange market using Twitter and financial news sites: a case of Istanbul stock exchange (BIST 100) (2020)
  4. A Comparative Study of SVM and LSTM Deep Learning Algorithms for Stock Market Prediction. (2019)
  5. URLLC-awared resource allocation for heterogeneous vehicular edge computing (2024)
  6. Deep learning-based feature engineering for stock price movement prediction (2019)
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  8. Mobilenet convolutional neural networks and support vector machines for palmprint recognition (2019)
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  12. Predicting the stock market index using stochastic time series ARIMA modelling: The sample of BSE and NSE (2019)
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  15. Risk-Aware Financial Portfolio Management with Distributional Deep Deterministic Policy Gradient (2021)
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0 of 26 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.