Stock Market Prediction Using LSTM-Attention Network

Stock market prediction is a complex and challenging task due to the volatile and non-linear nature of financial data. Traditional machine learning models often struggle to capture long-term dependencies and intricate patterns in stock price movements. In this study, we propose an attention-based deep learning model to enhance the accuracy of stock market forecasting. The Attention Network selectively focuses on critical features and temporal dependencies, improving predictive performance compared to conventional models such as LSTMs and CNNs. We utilize historical stock data, technical indicators, and sentiment analysis from financial news to train the model. The attention mechanism helps identify key trends and market signals by assigning different importance weights to various input features. Experimental results demonstrate that our proposed model outperforms baseline methods in terms of mean absolute error (MAE) and root mean square error (RMSE), indicating its robustness in financial time-series prediction. This approach provides valuable insights for traders and investors, enabling better decision-making in dynamic market conditions. Future work will explore the integration of reinforcement learning and hybrid models for further optimization.