NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION

This study presents an optimization procedure for the number of processing elements (neurons) of hidden layers to predict a stock price index using Evolutionary Artificial Neural Networks (EANN), in particular, for the Istanbul Stock Market price index (ISE) in order to contribute to the development of Intelligent Systems Methods for modeling several systems that are highly non-linear and uncertain. The US dollars/Turkish Lira (US/TRY) exchange rate, Euro/Turkish Lira (EUR/TRY) exchange rate, ISE National 100 (XU100) index, world oil price, and gold price were used as for a period of approximately 10 years’ daily data as inputs. Performance is benchmarked by mean squared error, normalized mean squared error; mean absolute error and the correlation coefficient. With the fixed neural network architecture and optimized parameters, evolutionary neural networks perform better performance values when the number of neurons used in hidden layers is optimized.

Keywords:

Evolutionary algorithms, artificial neural networks, prediction stock price index,

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