Financial Prediction of Major Indices using Computational Efficient Artificial Neural Networks

Abstract

Two computational efficient artificial neural networks (ANNs) for the prediction of major financial indices are proposed. First, we propose a single layer functional link artificial neural network (FLANN) for this purpose. FLANN has a simple structure in which the nonlinearity is introduced by the functional expansion of the input pattern using trigonometric polynomials. The second ANN proposed is a Chebyshev neural network (chNN) in which the functional expansion is carried out using Chebyshev polynomials. Performance comparison of the two ANNs with regards to a multilayer perceptron (MLP) were carried out through extensive computer simulations. It is shown that the proposed ANNs outperform the MLP for the prediction of the three financial indices.