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.

I. Introduction

The successful prediction of future price of stock index and its direction of movement may not only helpful for the investors to make effective trading strategies, but also for policy maker to monitor stock market. Keeping track of upswings and downswings over the history of individual stocks will reduce the uncertainty associated to investment decision making. Investors can choose the best times to buy and sell the stock through proper analysis of the stock trends. Hence developing more realistic models to predict the rise and fall situations in the stock price index movement is a big challenge for most of the investors and professional analysts. In literature a number of models combining technical analysis with computational intelligent techniques are available for prediction of stock price index movements [1]–[4].