Stock Price Prediction Model Using Deep Learning Optimization Based on Technical Analysis Indicators

Abstract

Stock price prediction is one of the processes of analyzing and determining stock prices in the future. With technical analysis, future stock price predictions can be predicted through the pattern of fluctuations in the stock price in the past. In this study, the researcher predicts the stock price for the next week using the Deep Learning method, namely the Multilayer Perceptron, and combined with the day-shifting method. To expect the results of this stock, the author also observes the model's usefulness and proposes a Mean Error to Mean Price Ratio (MEMPR) to increase the insights processed by the model. Then to find out the accuracy of stock price predictions for each algorithm, testing is carried out using stock data which consists of new data which is then carried out by a training process to get an absolute error value. The experimental results show that the model can predict stock prices with an R2 metric of 0.995.