Financial prediction of major indices using computational efficient artificial neural networks

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.