Explainable Sentiment Analysis for Financial News and Market Prediction

Abstract: Financial news significantly influences investor behavior and market movements. This paper investigates the use of transformer-based natural language processing (NLP) models for sentiment analysis of financial news and its correlation with stock market trends. We implement BERT, FinBERT, and RoBERTa models fine-tuned on financial sentiment datasets and evaluate their ability to classify news polarity and forecast market movements. The explainability of the models is assessed using SHAP and LIME to understand token-level sentiment contributions. Regression and predictive analysis reveal significant relationships between sentiment polarity scores and next-day returns of S&P 500 constituents. FinBERT outperforms other models, achieving an F1-score of 89.2% and showing the highest correlation (R² = 0.76) between sentiment and market returns. Keywords: Sentiment Analysis, Financial News, Market Prediction, FinBERT, Explainable AI, SHAP, LIME, NLP, BERT, RoBERTa