# Dynamic Hedging Strategies in Derivatives Markets with LLM-Driven Sentiment and News Analytics

arXiv: https://arxiv.org/abs/2504.04295 · HTML: https://mlsanity.com/paper/2504.04295/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 25 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding](https://mlsanity.com/paper/2009.05387/) (2020)
- [fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions](https://mlsanity.com/paper/2406.07456/) (2024)
- [L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset](https://mlsanity.com/paper/2103.11408/) (2021)
- [Investigating Online Financial Misinformation and Its Consequences: A Computational Perspective](https://mlsanity.com/paper/2309.12363/) (2023)
- [Generative AI for Business Strategy: Using Foundation Models to Create Business Strategy Tools](https://mlsanity.com/paper/2308.14182/) (2023)
- [Hedging Beyond the Mean: A Distributional Reinforcement Learning Perspective for Hedging Portfolios with Structured Products](https://mlsanity.com/paper/2407.10903/) (2024)
- [PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search](https://mlsanity.com/paper/2207.09068/) (2022)
- [Local hedging approximately solves Pandora's box problems with nonobligatory inspection](https://mlsanity.com/paper/2410.19011/) (2024)
- [Continuous Prompt Generation from Linear Combination of Discrete Prompt Embeddings](https://mlsanity.com/paper/2312.10323/) (2023)
- [Mathematics of Differential Machine Learning in Derivative Pricing and Hedging](https://mlsanity.com/paper/2405.01233/) (2024)
- [Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information](https://mlsanity.com/paper/2405.17470/) (2024)
- [NeuralBeta: Estimating Beta Using Deep Learning](https://mlsanity.com/paper/2408.01387/) (2024)
- [Market Making and Pricing of Financial Derivatives based on Road Travel Times](https://mlsanity.com/paper/2305.02523/) (2023)
- [Repetitive Dilemma Games in Distribution Information Using Interplay of Droop Quota: Meek's Method in Impact of Maximum Compensation and Minimum Cost Routes in Information Role of Marginal Contribution in Two-Sided Matching Markets](https://mlsanity.com/paper/2403.18837/) (2024)
- [Hedging American Put Options with Deep Reinforcement Learning](https://mlsanity.com/paper/2405.06774/) (2024)
- [Optimizing Deep Reinforcement Learning for American Put Option Hedging](https://mlsanity.com/paper/2405.08602/) (2024)
- [Hedging Is Not All You Need: A Simple Baseline for Online Learning Under Haphazard Inputs](https://mlsanity.com/paper/2409.10242/) (2024)
- [Hedging and Pricing Structured Products Featuring Multiple Underlying Assets](https://mlsanity.com/paper/2411.01121/) (2024)
- [A New Way: Kronecker-Factored Approximate Curvature Deep Hedging and its Benefits](https://mlsanity.com/paper/2411.15002/) (2024)

## Built on by (most cited first, 2 of 2)

- [ToolGate: Contract-Grounded and Verified Tool Execution for LLMs](https://mlsanity.com/paper/2601.04688/) (2026)
- [SkillForge: Compositional Skill Synthesis with Verification-in-the-Loop for Generating Formally Verified Dafny Programs](https://mlsanity.com/paper/2608.29841/) (2026)

## Abstract (arXiv)

Dynamic hedging strategies are essential for effective risk management in derivatives markets, where volatility and market sentiment can greatly impact performance. This paper introduces a novel framework that leverages large language models (LLMs) for sentiment analysis and news analytics to inform hedging decisions. By analyzing textual data from diverse sources like news articles, social media, and financial reports, our approach captures critical sentiment indicators that reflect current market conditions. The framework allows for real-time adjustments to hedging strategies, adapting positions based on continuous sentiment signals. Backtesting results on historical derivatives data reveal that our dynamic hedging strategies achieve superior risk-adjusted returns compared to conventional static approaches. The incorporation of LLM-driven sentiment analysis into hedging practices presents a significant advancement in decision-making processes within derivatives trading. This research showcases how sentiment-informed dynamic hedging can enhance portfolio management and effectively mitigate associated risks.