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

Year 2013, Volume: 5 Issue: 1, 12 - 21, 01.06.2013

Asil Alkaya

Abstract

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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