Accurate stock market forecasting remains challenging due to the nonlinear, noisy, and volatile nature of financial markets. Beyond historical prices, market movements are influenced by financial news and investor sentiment. This study proposes a hybrid financial bidirectional encoder representations from transformers with convolutional neural network and long short-term memory (FinBERT-CNN-LSTM) model for forecasting the Standard & Poor’s 500 (S&P 500) index by integrating financial news sentiment with historical market data. FinBERT generates daily aggregated sentiment scores, which are combined with market features and processed through a CNN-LSTM architecture. The CNN captures local patterns and short-term interactions, while the LSTM models temporal dependencies in financial time series. Experimental results show that the proposed model outperforms benchmark models, achieving a root mean square error (RMSE) of 0.0119, mean absolute error (MAE) of 0.0091, mean absolute percentage error (MAPE) of 2.10%, and directional accuracy (DA) of 63.4%. It also achieves a cumulative return of 15.8% and a Sharpe ratio of 1.35. Rolling-window validation and Diebold–Mariano (DM) testing further support the statistical significance of the improvements. These findings demonstrate the effectiveness of combining financial sentiment and deep learning (DL) for stock market forecasting.