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<p>This study explores the integration of generative artificial intelligence (AI) into financial sentiment analysis, focusing on enhancing market behavior predictions using advanced large language models (LLMs). A novel sentiment analysis framework is developed, leveraging cutting-edge LLMs and generative AI for data augmentation. The approach incorporates optimized word embeddings and fine-tuning techniques such as Few-shot Learning and Low-Rank Adaptation (LoRA) to handle the linguistic complexities of financial discourse. The framework is evaluated using five performance metrics, demonstrating improved accuracy and efficiency. These findings highlight the transformative potential of LLMs in financial decision-making and sentiment-driven trading strategies.</p>

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From text to trade: harnessing the potential of generative AI for investor sentiment analysis in financial markets through large language models

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Nouri Hicham,
  • Nassera Habbat

摘要

This study explores the integration of generative artificial intelligence (AI) into financial sentiment analysis, focusing on enhancing market behavior predictions using advanced large language models (LLMs). A novel sentiment analysis framework is developed, leveraging cutting-edge LLMs and generative AI for data augmentation. The approach incorporates optimized word embeddings and fine-tuning techniques such as Few-shot Learning and Low-Rank Adaptation (LoRA) to handle the linguistic complexities of financial discourse. The framework is evaluated using five performance metrics, demonstrating improved accuracy and efficiency. These findings highlight the transformative potential of LLMs in financial decision-making and sentiment-driven trading strategies.