Stock price index movement classification using a CEFLANN with extreme learning machine

Abstract

Recently, an efficient learning algorithm called extreme learning machine (ELM) has been proposed for training of single hidden layer feed forward neural networks (SLFNs). ELM has shown good generalization performances for many real applications with an extremely fast learning speed. This study proposes a computational efficient functional link artificial neural network (CEFLANN) trained with ELM for addressing the problem of classifying stock price index movements as up and down movements. The proposed model is also compared with two other popular networks like Chebyshev FLANN and RBF, used mostly in classification problems. Again the performance of all the networks are compared with back propagation and ELM based learning over two benchmark financial data sets. Experimental results show that the performance of proposed model outperforms the other models.