Transforming sentiment analysis in the financial domain with ChatGPT

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 addition 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.