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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.04295 |
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| _version_ | 1866915229849354240 |
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| author | Yang, Jie Tang, Yiqiu Li, Yongjie Zhang, Lihua Zhang, Haoran |
| author_facet | Yang, Jie Tang, Yiqiu Li, Yongjie Zhang, Lihua Zhang, Haoran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04295 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Hedging Strategies in Derivatives Markets with LLM-Driven Sentiment and News Analytics Yang, Jie Tang, Yiqiu Li, Yongjie Zhang, Lihua Zhang, Haoran Computation and Language Computational Engineering, Finance, and Science 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. |
| title | Dynamic Hedging Strategies in Derivatives Markets with LLM-Driven Sentiment and News Analytics |
| topic | Computation and Language Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2504.04295 |