Dynamic Financial Sentiment Analysis and Market Forecasting through Large Language Models

Sentiment analysis is essential for determining public opinion, customer feedback, and decision-making in different disciplines. While traditional sentiment analysis investigates general sentiment classification, aspect-based sentiment analysis with the finer aspect of sentiment identification delves into specialized sentiments directed toward specific product or service elements. In finance, sentiment analysis provides excellent value in market-related conditions, including trend forecasting, stock price forecasting, and investment decisions. However, in current-day research, financial sentiment analysis fails in two respects: the ability to analyze vast and dynamic unstructured financial discourse and, second, to track the domain-specific connotations. In this paper, we tackle these problems by utilizing three advanced models for financial sentiment classification: FinBERT, GPT-4, and T5. While evaluation metrics considered precision, recall, and F1-score, the results show that GPT-4 proved the best by achieving 93.5% precision, 92.8% recall, and an F1-score of 93.1%. This indicates the incredible ability of GPT-4 in generalization between different financial contexts. FinBERT comes next in prediction since it holds up best in structured financial texts, achieving an F1-score of 90.8%. T5, while showing strong generative capacity, was inhibited in its recall and generalization. This points out each model's principal strength and weakness, suggesting that GPT-4 is preferably suited for real-time tracking of financial sentiment, FinBERT for more structured financial analysis, and T5 for generating financial sentiment and explainable AI-type applications. This work advances the field by furnishing selections for ideal model choices based on application necessities in financial sentiment analysis.