Browsing by Author "Ramuada, Vhahangwele Cedrick"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Forecasting stock returns: A comparison of five models
    (Stellenbosch : Stellenbosch University, 2018-12) Ramuada, Vhahangwele Cedrick; Sanders, J. W.; Becker, Ronald I.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Mathematics.
    ENGLISH ABSTRACT : Forecasting the movement of stock returns prices has been of interest to researches for many decades. Due to the complex and chaotic nature of the stock market, it has been difficult for researches to find a model which can be used to accurately predict the movement of stock returns prices. Many statistical models have been proposed for forecasting the direction of movement of stock returns prices. The objective of this study was to use ARMA type models and an Artificial Intelligence Neural Network model to predict the direction of movement of stock returns prices of four JSE listed companies, namely, Netcare Group Ltd, Santam Ltd, Sanlam Group Ltd, and Nedbank Group. The models were assessed in terms of their ability to predict whether the next day’s returns price will go down or up. Four ARMA-type models, namely, ARMA-Maximum Likelihood, ARMAState Space, ARMA-Metropolis Hastings, AR(3)-AVGARCH(1,1)-Student-t model and an Artificial Neural Network (ANN) model were implemented to try to predict the direction of movement of stock returns prices. Historical (past) stock returns prices were used to make inference about future directional movement of stock returns prices. Empirical results show that the ARMA-Maximum Likelihood, ARMA-State Space, AR(3)-AVGARCH(1,1)- Student-t model, and Artificial Neural Network (ANN) models have a strong ability to predict whether the next day’s returns price will go down or up with acceptable accuracy. However, the ARMA-Metropolis Hastings model performed very poorly, its highest accuracy was a mere 68%. Overall, empirical results show that the Artificial Neural Network model was superior or outperformed all the ARMA-type models, the highest accuracy achieved by the model was 89%. The results of the Superior Ability Test also showed that the ANN model was indeed superior to the Box-Jenkins ARMA type models in at least 5 cases.