Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an additional evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35\% enhanced performance in sentiment classification and a 36\% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.

翻译：金融情感分析在解读市场趋势和指导战略交易决策中发挥着关键作用。尽管已有先进的深度学习技术和语言模型被用于优化金融情感分析，本研究另辟蹊径，重点探究大规模语言模型（特别是ChatGPT 3.5）在金融情感分析中的潜力，并针对外汇市场（forex）进行深入考察。采用零样本提示方法，我们在精心整理的外汇相关新闻标题数据集上测试了多种ChatGPT提示，并使用精确率、召回率、F1分数以及情感类别的平均绝对误差（MAE）等指标衡量性能。此外，我们通过探究预测情感与市场回报之间的相关性，作为额外的评估方式。与金融文本情感分析领域公认的FinBERT模型相比，ChatGPT在情感分类性能上提升了约35%，在与市场回报的相关性上提高了36%。通过强调提示工程的重要性（尤其在零样本情境下），本研究凸显了ChatGPT在显著提升金融应用情感分析方面的潜力。通过共享所使用的数据集，我们旨在推动金融服务领域的进一步研究与发展。