Apple, Microsoft, and Amazon stock price prediction based on ARIMA and LSTM

Abstract

Along with the advancement in technology and data analysis, efforts have been made to use various factors to predict the stock market to improve portfolio performance and ensure an efficient market. This study explores two stock forecasting models, Long Short-Term Memory(LSTM) and Auto Regressive Integrated Moving Average (ARIMA), and evaluates their performance on the same datasets of historical stock prices of Apple, Microsoft, and Amazon. While ARIMA exhibited superior performance in terms of Root Mean Squared Error(RMSE) and R2 metrics, the research acknowledges inherent limitations, such as the exclusion of potential external influencing factors and the simplistic approach toward LSTM optimization. Despite ARIMA's commendable forecasting ability in this context, the dynamic and non-linear nature of stock prices suggests the potential of hybrid models and broader datasets for future research. The findings underscore the significance of selecting appropriate forecasting tools in the volatile domain of stock investments and pave the way for a more holistic and informed approach to stock price prediction.