A forecasting approach for stock index future using grey theory and neural networks

Abstract

Previously used quantitative indices for predicting stock prices are not really suitable, and the requirement for a large amount of input data slows down the convergence of a neural network model. Therefore, this research attempts to develop a better prediction model by the integration of neural network technique and grey theory for the SIMEX Taiwan stock index future. In this research, the grey theory applied include grey forecast model and grey relationship analysis. The grey forecast model, GM(1,1), was applied to predict the next day's stock index future. To examine the influence of dimension of the model to prediction accuracy, seven different kinds of dimension 5, 6, 8, 10, 12, 14, and 15 were tested. The generated data were then regarded as new technical indices in grey relationship analysis and prediction of neural network. Grey relationship analysis was used to filter the most important quantitative technical indices. Finally, a recurrent neural network was developed to train and predict the price trend of stock index future. In the network structure, the price trend of stock index future is the output and the values gained from previous processing in grey relationship analysis is the input. The conclusion shows our models can provide good prediction for this problem.