Transforming sentiment analysis in the financial domain with ChatGPT

• 出版年: Machine Learning with Applications
• 主要な著者: Georgios Fatouros, John Soldatos, Kalliopi Kouroumali, Georgios Makridis, Dimosthenis Kyriazis
• フォーマット: 論文
• 言語: 英語
• 出版事項: Elsevier 2023-12-01
• 主題: ChatGPT; Artificial intelligence; Finance; Sentiment analysis; Risk assessment
• オンライン･アクセス: http://www.sciencedirect.com/science/article/pii/S2666827023000610

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.