Stock index prediction is one of the important tasks in the domain of computational finance. A number of tools have been developed by various groups of researchers and are being used by many analysts to identify the future price index. However, due to the high degree of non-linearity of the problem and surrounded by many optimal solutions, this paper proposes Radial Basis Function Neural Networks (RBFNNs) learning using Genetic Algorithm (GA) to predict the stock price index and at the same time the connection weights between the layers and thresholds are optimised using GA. Further, potential indicators are used to make the model robust in terms of its efficiency and accuracy. The accuracy is compared to MLP-BP and GA models. Finally, the experimental results show that the optimized RBFNNs model is the optimum model in comparison to other conventional models.

Stock Index Prediction, RBF Neural Network, Genetic Algorithm, Real coding

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