A Novel Prediction Method for Stock Index Applying Grey Theory and Neural Networks

This paper presents a better prediction model by the integration of neural network technique and grey theory for the stock index. In this paper, the grey theory applied include grey forecast model and grey relationship analysis. A GM(l, l) grey forecast model was applied to predict the next day’s stock index. Grey relationship analysis was used to filter the most important quantitative technical indices. To examine the influence of dimension of the model to prediction accuracy, seven different kinds of dimension 5, 6, 8, 10, 12, 14, and15 were tested. The generated data were then regarded as new technical indices in grey relationship analysis and prediction of neural network. Finally, a Recurrent Neural Network was developed to train and predict the price trend of stock index. The conclusion shows our models can provide good prediction for this problem.