CONFIGURING ARTIFICIAL NEURAL NETWORKS FOR STOCK MARKET PREDICTIONS
研究如何配置人工神经网络以预测股市，以罗马尼亚BET指数为例，并用克罗地亚股市数据验证，发现BFGS训练算法在收敛和泛化上表现良好。
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.