Stock Price Prediction Using Back Propagation Neural Network Based on Gradient Descent with Momentum and Adaptive Learning Rate
Accurate financial predictions are challenging and attractive to individual investors and corporations. Paper proposes a gradient-based back propagation neural network approach to improve optimization in stock price predictions. The use of gradient descent in BPNN method aims to determine the parameter of learning rate, training cycle adaptively so as to get the best value in the process of stock data training in order to obtain accuracy in prediction. To test BPNN method, mean square error is used to prediction result and data reality. The smallest MSE value shows better results compared to larger MSE value in predictions.