A comparative study of radial basis function network with different basis functions for stock trend prediction

This paper proposes a radial basis function (RBF) network trained using ridge extreme learning machine to predict the future trend from the past stock index values. Here the task of predicting future stock trend i.e. the up and down movements of stock price index values is cast as a classification problem. Recently extreme learning machine (ELM) is used as an efficient learning algorithm for single hidden layer feed forward neural networks (SLFNs). ELM has shown good generalization performances for many real applications with an extremely fast learning speed. To achieve better performance, an improved ELM with ridge regression called ridge ELM (RELM) is proposed in the study. Gaussian function is the most popular basis function used for RBFN in many applications. But the basis function may not be appropriate for all the applications. Hence the effect of the RBF network with seven different basis functions is compared for addressing the classification task. Again the performance of the RBF network is also compared with back propagation and ELM based learning over two benchmark financial data sets. Experimental results show that evaluating all recognized basis functions suitable for RBF networks is advantageous.