Long Short Term Memory Using Stochastic Gradient Descent and Adam for Stock Prediction

Abstract

The stock market is a place to carry out stock buying and selling transactions, the expected return of course has a profitable difference. Predicting stock prices can be done in various ways, one of which is by using deep learning models. Long Short Term Memory (LSTM) is a method that can be used to predict time series data. This method is a development of the Recurrent Neural Network (RNN), so this method is more complicated and powerful. To conduct training on the LSTM model, optimization is needed to minimize errors. There are lots of optimizations that can be used, but in this research, we use SGD and Adam. Several parameters such as learning rate 0.01, 0.001, 0.0001 and several variations of epochs such as epoch 25, epoch 50, epoch 100, epoch 200, epoch 400, and epoch 1000 were used in this study. The research data used are stock data of BBRI, BBNI, BMRI, and BBTN. This study also tries to predict stock prices on the next day using 5 historical stock price data, the result is that LSTM SGD and LSTM Adam succeeded in predicting the next day