GroupSHAP-Guided Integration of Financial News Keywords and Technical Indicators for Stock Price Prediction

TL;DR

This paper introduces a GroupSHAP-guided framework that leverages semantic keyword groups from financial news to improve stock price prediction accuracy and interpretability, reducing computational costs compared to traditional SHAP methods.

Contribution

It presents the first application of GroupSHAP in financial forecasting, combining semantic grouping with SHAP to enhance interpretability and predictive performance.

Findings

Achieved 32.2% reduction in MAE for S&P 500 forecasting

Achieved 40.5% reduction in RMSE compared to benchmarks

Demonstrated effective use of semantic keyword groups for stock prediction

Abstract

Recent advances in finance-specific language models such as FinBERT have enabled the quantification of public sentiment into index-based measures, yet compressing diverse linguistic signals into single metrics overlooks contextual nuances and limits interpretability. To address this limitation, explainable AI techniques, particularly SHAP (SHapley Additive Explanations), have been employed to identify influential features. However, SHAP's computational cost grows exponentially with input features, making it impractical for large-scale text-based financial data. This study introduces a GRU-based forecasting framework enhanced with GroupSHAP, which quantifies contributions of semantically related keyword groups rather than individual tokens, substantially reducing computational burden while preserving interpretability. We employed FinBERT to embed news articles from 2015 to 2024, clustered them into coherent semantic groups, and applied GroupSHAP to measure each group's contribution to stock price movements. The resulting group-level SHAP variables across multiple topics were used as input features for the prediction model. Empirical results from one-day-ahead forecasting of the S&P 500 index throughout 2024 demonstrate that our approach achieves a 32.2% reduction in MAE and a 40.5% reduction in RMSE compared with benchmark models without the GroupSHAP mechanism. This research presents the first application of GroupSHAP in news-driven financial forecasting, showing that grouped sentiment representations simultaneously enhance interpretability and predictive performance.