Stock price forecast with deep learning LSTM and econometric ARIMA models

Abstract

The forecast of stock market prices is very important information for investors who have the intention to invest in the stock market. There are forecast models designed to make predictions. In this paper, I propose different ones from conventional econometric models to machine learning models for forecasting stock prices. At the end, the results of both of them will be discussed and conclusions will be made upon which one is better as a forecasting tool for APPLE INC. In this paper, the tools for prediction stock prices are ARIMA model and recurrent neural network layers such as LSTM network. The results show that LSTM outperformed ARIMA model.