Stock Price Prediction Using Formula From Data Scale Analysis Extrapolation Method

Abstract

Public awareness about stocks is increasing every year. This needs to be balanced with advances in science to help investors make more measurable and systematic decisions. The purpose of this study is to apply Extrapolation of Data Scale Analysis (DSA) in predicting stock prices. The DSA Extrapolation method will produce a new formula called JIC-FLY 3 which is used to process big data in predicting prices at a certain time. The population in this study is the share price of the telecommunications sub-sector with EXCL (PT XL Axiata Tbk.) as a sample. The error value measurement method used is the Mean Absolute Percentage Error (MAPE). The result of this study is that the DSA 19 extrapolation method using the JIC-FLY 3 formula is able to predict the share price of the telecommunication subsector with the best error giving the smallest error value of 0.193%.