Leveraging Large Language Models and Prompt Settings for Context-Aware Financial Sentiment Analysis

Carefully crafted prompts can significantly enhance the accuracy and effectiveness of sentiment classification models. This paper explores the use of prompt engineering and large language models for financial sentiment analysis on financial reports of companies. Zero-shot and few-shot with prompts are designed to extract sentiment and contextual information. AI-generated synthetic examples were created for few-shot settings. Human-evaluated results are compared with four LLMs. Results show varying performance and output quality among LLMs, influenced by prompt design, report content, and task complexity. The LLMs' responses varied in length, detail, and style, affecting their readability and usefulness. The paper discusses the implications and limitations of these findings, suggesting future research directions.