Transforming Sentiment Analysis in the Financial Domain with ChatGPT

TL;DR

This paper explores the use of ChatGPT 3.5 with zero-shot prompting for financial sentiment analysis, demonstrating significant performance improvements over traditional models like FinBERT in forex market applications.

Contribution

It introduces a novel application of large language models with prompt engineering for financial sentiment analysis, especially in zero-shot settings, and provides a curated dataset for future research.

Findings

ChatGPT outperforms FinBERT by 35% in sentiment classification accuracy.

A 36% higher correlation between ChatGPT sentiment predictions and market returns.

Emphasizes the importance of prompt engineering in zero-shot financial NLP tasks.

Abstract

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.