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Configuring Artificial Neural Networks for stock market predictions

    Gheorghe Ruxanda Affiliation
    ; Laura Maria Badea Affiliation

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

How to Cite
Ruxanda, G., & Badea, L. M. (2014). Configuring Artificial Neural Networks for stock market predictions. Technological and Economic Development of Economy, 20(1), 116-132. https://doi.org/10.3846/20294913.2014.889051
Published in Issue
Mar 26, 2014
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This work is licensed under a Creative Commons Attribution 4.0 International License.