A Comparison of LSTM and Some Machine Learning Regressors for the Prediction of Close Prices of the Nigerian Stock Market

Abstract:

A lot of work has been done on stock market predictions in the literature. A salient fact is that stock market prediction is a dynamic phenomenon. This is due to the fact that once a prediction model is known, it becomes necessary to device a new model due to the dynamic nature of the market. Traditional predicting method is characterized by the use of fundamental and technical analysis. Fundamental analysis is based on economy indices of the market while the technical analysis is based on history and trends of the market. In this paper, we present the application of recurrent neural network for the prediction of the close price of stocks in the Nigerian stock market. We confirm previous results that states that deep learning algorithms offer a higher performance than traditional machine learning algorithms like support vector regressor, catboost regressor and multi linear regressor. The experimental result obtained shows that RNN performed best with the least mean squared error of 0.036577 while multi linear regressor has mean squared error of 0.049327, support vector regressor has mean squared error of 0.066206 and catboost regressor has mean squared error of 0.091510

Keywords: Recurrent Neural Network (RNN), Support Vector Regressor (SVR), Multi linear regressor, Catboost regressor