Configuring Artificial Neural Networks for stock market predictions

Abstract

Making accurate predictions for stock market values with advanced non-linear methods creates opportunities for business practitioners, especially nowadays, with highly volatile stock market evolutions. Well suited for approaching non-linear problems, Artificial Neural Networks provide a number of features which make possible reasonably accurate forecasts. But, like the old Latin saying “Primus inter pares”, not all Artificial Neural Networks perform the same, end results depending very much on the network architecture and, more specifically, on the chosen training algorithm. This paper provides suggestions on how to configure Artificial Neural Networks for performing stock market predictions, with an application on the Romanian BET index. Final results are confirmed by testing the trained networks on the Croatian Stock Market data. End remarks entitle Broyden-Fletcher-Goldfarb-Shanno training algorithm as a good choice in terms of model convergence and generalization capacity.

Keyword : Prediction, Artificial Neural Networks, nonlinear programming, gradient descent, BFGS, numerical differentiation, stock exchange market