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<p>In the fast-evolving artificial intelligence era, the intersection of natural language processing and financial analysis has attracted significant attention, primarily due to its potential to provide valuable insights into financial market behavior. Sentiment analysis of financial news articles is a crucial aspect of this intersection, providing cues about market sentiment that may affect stock price dynamics. Traditional sentiment analysis methods often rely on rules or machine learning algorithms trained on labeled datasets, but these methods face challenges in capturing the context within the text. This paper proposes a framework that incorporates prompt engineering strategies, including a novel Domain Knowledge Chain-of-Thought (DK-CoT) strategy, integrating domain-specific financial knowledge with chain-of-thought reasoning, designed to leverage and enhance the performance of large language models (LLMs) in financial news sentiment analysis. DK-CoT has been compared with various prompt engineering techniques, including zero-shot, few-shot, and chain-of-thought, as well as other benchmark models like BERT and RoBERTa. Through comprehensive experiments and evaluations, we introduce the weighted F1 score as a more practical metric, emphasizing the disproportionate impact of negative news on financial markets, which better reflects real-world financial dynamics, as negative sentiments often lead to more significant market reactions than positive or neutral sentiments. Experimental results have shown that DK-CoT adopted in an LLM called GLM is effective in improving the performance and reliability of financial news sentiment analysis. Our findings provide insights into optimal prompt designs and highlight the importance of incorporating financial knowledge to uplift LLM performance while reducing the need for extensive computational resources and fine-tuning.</p>

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Leveraging large language model as news sentiment predictor in stock markets: a knowledge-enhanced strategy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月9日

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  • Weisi Chen,
  • Wulong Liu,
  • Jiaxin Zheng,
  • Xu Zhang

摘要

In the fast-evolving artificial intelligence era, the intersection of natural language processing and financial analysis has attracted significant attention, primarily due to its potential to provide valuable insights into financial market behavior. Sentiment analysis of financial news articles is a crucial aspect of this intersection, providing cues about market sentiment that may affect stock price dynamics. Traditional sentiment analysis methods often rely on rules or machine learning algorithms trained on labeled datasets, but these methods face challenges in capturing the context within the text. This paper proposes a framework that incorporates prompt engineering strategies, including a novel Domain Knowledge Chain-of-Thought (DK-CoT) strategy, integrating domain-specific financial knowledge with chain-of-thought reasoning, designed to leverage and enhance the performance of large language models (LLMs) in financial news sentiment analysis. DK-CoT has been compared with various prompt engineering techniques, including zero-shot, few-shot, and chain-of-thought, as well as other benchmark models like BERT and RoBERTa. Through comprehensive experiments and evaluations, we introduce the weighted F1 score as a more practical metric, emphasizing the disproportionate impact of negative news on financial markets, which better reflects real-world financial dynamics, as negative sentiments often lead to more significant market reactions than positive or neutral sentiments. Experimental results have shown that DK-CoT adopted in an LLM called GLM is effective in improving the performance and reliability of financial news sentiment analysis. Our findings provide insights into optimal prompt designs and highlight the importance of incorporating financial knowledge to uplift LLM performance while reducing the need for extensive computational resources and fine-tuning.