Integrating FinBERT based news sentiment and technical indicators for LSTM stock price forecasting

Abstract

Accurate stock price forecasting is important for investment and risk management but remains challenging due to complex interactions among market dynamics, firm-specific information, and investor sentiment. Although technical indicators capture historical price patterns, they may not fully reflect information conveyed through financial news. This study develops an LSTM-based framework that integrates technical indicators with FinBERT-derived news sentiment for next-day stock price forecasting. The model uses closing price, EMA26, MACD, RSI14, and daily sentiment scores extracted from 58,466 GDELT news headlines for Amazon, Apple, Google, Microsoft, and NVIDIA from 2019 to 2023. Company-level sentiment is temporally aligned with market features, and 15-day sequences are constructed to predict the normalized next-trading-day closing price. Out-of-sample evaluation from July to December 2023 reveals heterogeneous performance across firms: Apple achieves the lowest RMSE and MAE (0.0147 and 0.0110), while NVIDIA records the highest (0.0245 and 0.0201). Predictions generally capture temporal price dynamics but are less responsive to abrupt turning points. The findings demonstrate a systematic approach to integrating financial news sentiment and technical indicators for stock price forecasting while highlighting substantial cross-firm variation in predictive performance.