This research introduces a novel framework that uses large language models to analyze sentiment from news and social media to optimize hedging strategies in derivatives markets.

• Integrates real-time sentiment analysis from diverse textual sources to inform trading decisions
• Captures market sentiment indicators that traditional quantitative models miss
• Provides adaptive hedging approaches that respond to changing market conditions
• Enhances risk management capabilities through comprehensive news analytics

Security Impact: By improving risk assessment and hedging accuracy, this framework significantly strengthens financial security postures, helping institutions better protect against market volatility and unexpected market movements.

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