Forecasting stock returns: A comparison offivemodels

[摘要] 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 thestock market, it has been difficult for researches to find a model which canbe used to accurately predict the movement of stock returns prices. Manystatistical 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 predictthe direction of movement of stock returns prices of four JSE listed companies, namely, Netcare Group Ltd, Santam Ltd, Sanlam Group Ltd, andNedbank 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-tmodel and an Artificial Neural Network (ANN) model were implementedto 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 theARMA-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 upwith acceptable accuracy. However, the ARMA-Metropolis Hastings modelperformed very poorly, its highest accuracy was a mere 68%. Overall, empirical results show that the Artificial Neural Network model was superioror outperformed all the ARMA-type models, the highest accuracy achievedby the model was 89%. The results of the Superior Ability Test also showedthat the ANN model was indeed superior to the Box-Jenkins ARMA typemodels in at least 5 cases.

[发布机构] Stellenbosch University