  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.
