IMPLEMENTATION OF THE CONVOLUTIONAL NEURAL NETWORK METHOD FOR PREDICTING LQ45 STOCK PRICES

Abstract

Stock are securities of ownership of a company. Investments in the stock market on average can produce a return rate of 10-30% per year, this amount is about two to three times higher than the rate of return on deposits or savings in banks which are only 5-10 % every year. One problem is the stock price is fluctuating or changing due to certain factors. This study compares several window size data with different amounts of data, aiming to find window size data with a more accurate amount of data for stock price predictions. Convolutional neural network algorithm with window size data of 7 days, 14 days, 21 days and 28 days in the amount of data 1 year and 2 years for stock price predictions. The results of this study are the convolutional neural network algorithm with a data window size of 7 days at the amount of data 2 years is more accurate than the window size data and the amount of other data. Because the smallest error result is 0.000201587.