A Comparison of Recurrent Neural Networks Models and Econometric Models for Stock Market Predictions

Abstract: It is well known that the stock market is highly volatile, so stock price prediction is a very challenging task. However, in order to make a profit or to understand the equity market, many investors and researchers use various statistical, econometric, and neural network models to make the best stock price predictions possible. In this thesis the aim is to compare the predictability of two econometric models, the exponential moving average (EMA) and auto regressive integrated moving average (ARIMA) models, and two neural network models, a simple recurrent neural network (RNN) and the long short term memory model (LSTM) model. The comparison is primarily made using the Tesla company as the underlying stock. While using mean square error (MSE) as a measure of performance, the LSTM model consistently outperformed the other three models.