Empowering Stock Trading through Macroeconomic Events: A Deep Learning-Based NLP Framework

Macroeconomic announcements—such as central bank policy decisions, employment statistics, and inflation reports—have a profound impact on equity markets. However, extracting actionable insights from such textual disclosures remains a significant challenge due to the complexity, ambiguity, and temporally dynamic nature of economic language. In this paper, we propose a novel deep learning-based Natural Language Processing (NLP) framework designed to empower stock trading strategies through automated interpretation of macroeconomic events. Our approach integrates FinBERT—a transformer-based language model pretrained on financial corpora—with Long Short-Term Memory (LSTM) networks to capture both the semantic and sequential characteristics of economic news. We construct a large-scale dataset comprising over 5,000 timestamped macroeconomic headlines linked to sectoral movements of the S&P 500 index, enabling high-resolution mapping between news sentiment and market behavior. Empirical results demonstrate that our FinBERT-LSTM hybrid outperforms traditional sentiment-based baselines and standalone language models in both accuracy and directional hit rate. Specifically, the model achieves a 72.5% classification accuracy and exhibits enhanced robustness across economically sensitive sectors such as Information Technology and Financials. Furthermore, our framework provides interpretable insights into how specific types of macroeconomic events influence different market segments. This research contributes to the growing field of financial AI by introducing a context-aware, temporally adaptive architecture capable of real-time sentiment interpretation and market movement forecasting. Our findings open new avenues for building intelligent trading systems that respond to economic narratives with precision and interpretability.