Paper: arXiv 2509.12753

Authors: Feliks Bańka, Jarosław A. Chudziak

Abstract

In volatile financial markets, balancing risk and return remains a significant challenge. Traditional approaches often focus solely on equity allocation, overlooking the strategic advantages of options trading for dynamic risk hedging. This work presents DeltaHedge, a multi-agent framework that integrates options trading with AI-driven portfolio management. By combining advanced reinforcement learning techniques with an ensembled options-based hedging strategy, DeltaHedge enhances risk-adjusted returns and stabilizes portfolio performance across varying market conditions. Experimental results demonstrate that DeltaHedge outperforms traditional strategies and standalone models, underscoring its potential to transform practical portfolio management in complex financial environments. Building on these findings, this paper contributes to the fields of quantitative finance and AI-driven portfolio optimization by introducing a novel multi-agent system for integrating options trading strategies, addressing a gap in the existing literature.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper leverages advanced deep reinforcement learning and ensemble methods, indicating significant mathematical density, while the experimental setup with benchmarks, ablation studies, and claims of outperformance suggests robust empirical validation.

Research Flowchart

  flowchart TD
  R["Research Goal: Enhance risk-adjusted returns via options"] --> I["Market Data Inputs<br>Historical Prices & Volatility"]
  I --> M["Key Methodology: Multi-Agent RL Framework<br>DeltaHedge with Ensembled Hedging Strategy"]
  M --> C["Computational Processes<br>Simulated Trading & Portfolio Optimization"]
  C --> O["Key Findings & Outcomes<br>Outperforms traditional strategies<br>Stabilizes portfolio across market conditions"]

Cited References (42)

  1. Applying Informer for Option Pricing: A Transformer-Based Approach — → 2506.05565
  2. Large Language Models and the Elliott Wave Principle: A Multi-Agent Deep Learning Approach to Big Data Analysis in Financial Markets (2024) — link
  3. Toward Predictive Stock Trading with Hidformer Integrated into Reinforcement Learning Strategy (2024) — link
  4. Target-Oriented Multi-Agent Coordination with Hierarchical Reinforcement Learning (2024) — link
  5. Deep Learning for Options Trading: An End-To-End Approach — → 2407.21791
  6. Hedging Beyond the Mean: A Distributional Reinforcement Learning Perspective for Hedging Portfolios with Structured Products (2024) — link
  7. Dynamic asset allocation with asset-specific regime forecasts — → 2406.09578
  8. A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist — → 2402.18485
and 34 more
  1. Combining transformer based deep reinforcement learning with Black-Litterman model for portfolio optimization — → 2402.16609
  2. Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management — → 2402.00515
  3. Large Language Model based Multi-Agents: A Survey of Progress and Challenges (2024) — link
  4. Revolutionising Financial Portfolio Management: The Non-Stationary Transformer’s Fusion of Macroeconomic Indicators and Sentiment Analysis in a Deep Reinforcement Learning Framework (2023) — link
  5. Option pricing revisited: The role of price volatility and dynamics (2023) — link
  6. Implementing Portfolio Risk Management and Hedging in Practice — → 2309.15767
  7. TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance — → 2309.03736
  8. D-TIPO: Deep time-inconsistent portfolio optimization with stocks and options — → 2308.10556
  9. Option Return Predictability with Machine Learning and Big Data (2023) — link
  10. Systemic Risk of Optioned Portfolios: Controllability and Optimization (2022) — link
  11. A COMPREHENSIVE STUDY OF OPTION PRICING WITH TRANSACTION COSTS (2022) — link
  12. Transformers in Time Series: A Survey (2022) — link
  13. Derivatives-based portfolio decisions: an expected utility insight (2022) — link
  14. The Sharpe Ratio (2021) — link
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  17. FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance (2020) — link
  18. Deep reinforcement learning for automated stock trading: an ensemble strategy — → 2511.12120
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  20. Modelling Stock Markets by Multi-agent Reinforcement Learning (2020) — link
  21. MAPS: Multi-Agent reinforcement learning-based Portfolio management System (2020) — link
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  32. Enhanced Portfolio Optimization: Integrating Machine Learning and Risk Management Techniques (2024)
  33. Informer (2021)
  34. Options, Futures, and Other Derivatives (1989)

10 of 42 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.