Hybrid Deep Learning Framework for Stock Price Prediction Incorporating Technical and Macroeconomic Indicators

The complicated interaction of several elements makes accurate stock price prediction still difficult. This study suggests a hybrid deep learning model to improve stock market prediction by integrating Bidirectional Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs). Incorporating both technical and macroeconomic variables helps the model to adequately identify complex trends and interconnections inside the financial data. While the bidirectional LSTM models temporal dependencies, the CNN component shines in retrieving pertinent features from the input data. Experimental findings show that the suggested model beats conventional approaches, therefore offering traders and investors important new perspectives.