Interpretable Machine Learning and Sentiment Analysis for Enhanced Predictive Accuracy in Financial Markets

This chapter delves into the intersection of sentiment analysis and stock price prediction, highlighting the use of interpretable machine learning models to enhance transparency and accuracy. The study employs FinBERT, a specialized language model for financial sentiment analysis, to extract insights from news articles and predict stock price movements. Key topics include the methodology for data collection and preprocessing, feature engineering, and model selection. The research demonstrates the significant impact of sentiment scores on stock prices, particularly during periods of market volatility. The study also introduces SHAP analysis to interpret model predictions, providing a deeper understanding of the factors influencing stock price movements. The results show that interpretable models can match or surpass the predictive power of complex deep learning architectures while offering greater transparency. This approach empowers investors to make more informed and strategic decisions in the financial markets.