Long Short-Term Memory and Gated Recurrent Unit for Stock Price Prediction

Abstract

Stocks are a popular investment with high risk due to rapid price fluctuations that are difficult to predict. Many investors do not understand the analysis of buying and selling stocks, making them hesitant to invest. For this reason, an analytical technique is needed that can determine the movement of stock prices in order to carry out planning, risk management, and decision-making. Banking stocks are among the important and popular stock sectors. One of the go public banking stocks is Bank Rakyat Indonesia stock. This research applies Long Short-Term Memory and Gated Recurrent Unit to produce a model that can accurately predict the stock price of Bank Rakyat Indonesia. Based on the implementation, GRU is the best model with MSE value of 4958.9168, RMSE 70.4195, and MAPE 1.1699%. The GRU model predict that there will be a decrease in stock prices in the next month.