BibTex RIS Kaynak Göster

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

Yıl 2013, Cilt: 5 Sayı: 1, 12 - 21, 01.06.2013

Öz

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.

Kaynakça

  • Alkaya A. & Bayhan, G.M. (2009). The Classification of a Simulation Data of a Servo System via Evolutionary Artificial Neural Networks, International Conference on Intelligent Computing Proceedings, pp 48-54.
  • Ang J.H., Tan K.C. & Al-Mamun A. (2008). Training neural networks for classiŞcation using growth probability-based evolution, Neurocomputing ,71 3493–3508
  • Blum, A., (1992). Neural networks in C++: an object-oriented framework for building connectionist systems, John Wiley & Sons, Inc., pp. 86-103
  • Castillo-Valdivieso, P. A., Merelo J. J., & Prieto A. (2002). Statistical Analysis of the Parameters of a Neuro-Genetic Algorithm, IEEE Transactıons On Neural Networks, Vol. 13, No. 6.
  • Fine,T.L. (1999). Feedforward Neural Network Methodology, Springer, New York, pp. 129-194
  • Freitas A. (2002). A Survey of Evolutionary Algorithms for Data Mining and Knowledge, Advances in Evolutionary Computation, 2002 – Citeseer
  • Goldberg, D.E.(1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley
  • Mehrotra, K., Mohan, C. K. & Ranka, S. (1997). Elements of ArtiŞcial Neural Networks, MIT Press, Cambridge, MA.
  • Siebel, N. T., Krause, J., & Sommer, G. (2007). Efficient Learning of Neural Networks with Evolutionary Algorithms, Lecture Notes in Computer Science , Springer
  • Stepniewski, S.W. & Keane, A. J. (2006). Topology design of feedforward neural networks by genetic algorithms ,Lecture Notes in Computer Science, Springer
  • Wang, C. And Principe, J. C. (1999). Training Neural Networks With Additive Noise in The Desired Signal, IEEE Transactions on Neural Networks.
  • http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RBRTE&f=D
  • http://www.gold.org/investment/statistics/gold_price_chart
Yıl 2013, Cilt: 5 Sayı: 1, 12 - 21, 01.06.2013

Öz

Kaynakça

  • Alkaya A. & Bayhan, G.M. (2009). The Classification of a Simulation Data of a Servo System via Evolutionary Artificial Neural Networks, International Conference on Intelligent Computing Proceedings, pp 48-54.
  • Ang J.H., Tan K.C. & Al-Mamun A. (2008). Training neural networks for classiŞcation using growth probability-based evolution, Neurocomputing ,71 3493–3508
  • Blum, A., (1992). Neural networks in C++: an object-oriented framework for building connectionist systems, John Wiley & Sons, Inc., pp. 86-103
  • Castillo-Valdivieso, P. A., Merelo J. J., & Prieto A. (2002). Statistical Analysis of the Parameters of a Neuro-Genetic Algorithm, IEEE Transactıons On Neural Networks, Vol. 13, No. 6.
  • Fine,T.L. (1999). Feedforward Neural Network Methodology, Springer, New York, pp. 129-194
  • Freitas A. (2002). A Survey of Evolutionary Algorithms for Data Mining and Knowledge, Advances in Evolutionary Computation, 2002 – Citeseer
  • Goldberg, D.E.(1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley
  • Mehrotra, K., Mohan, C. K. & Ranka, S. (1997). Elements of ArtiŞcial Neural Networks, MIT Press, Cambridge, MA.
  • Siebel, N. T., Krause, J., & Sommer, G. (2007). Efficient Learning of Neural Networks with Evolutionary Algorithms, Lecture Notes in Computer Science , Springer
  • Stepniewski, S.W. & Keane, A. J. (2006). Topology design of feedforward neural networks by genetic algorithms ,Lecture Notes in Computer Science, Springer
  • Wang, C. And Principe, J. C. (1999). Training Neural Networks With Additive Noise in The Desired Signal, IEEE Transactions on Neural Networks.
  • http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RBRTE&f=D
  • http://www.gold.org/investment/statistics/gold_price_chart
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Diğer ID JA34CK56JE
Bölüm Makaleler
Yazarlar

Asil Alkaya Bu kişi benim

Yayımlanma Tarihi 1 Haziran 2013
Yayımlandığı Sayı Yıl 2013 Cilt: 5 Sayı: 1

Kaynak Göster

APA Alkaya, A. (2013). NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION. International Journal of Economics and Finance Studies, 5(1), 12-21.
AMA Alkaya A. NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION. IJEFS. Haziran 2013;5(1):12-21.
Chicago Alkaya, Asil. “NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION”. International Journal of Economics and Finance Studies 5, sy. 1 (Haziran 2013): 12-21.
EndNote Alkaya A (01 Haziran 2013) NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION. International Journal of Economics and Finance Studies 5 1 12–21.
IEEE A. Alkaya, “NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION”, IJEFS, c. 5, sy. 1, ss. 12–21, 2013.
ISNAD Alkaya, Asil. “NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION”. International Journal of Economics and Finance Studies 5/1 (Haziran 2013), 12-21.
JAMA Alkaya A. NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION. IJEFS. 2013;5:12–21.
MLA Alkaya, Asil. “NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION”. International Journal of Economics and Finance Studies, c. 5, sy. 1, 2013, ss. 12-21.
Vancouver Alkaya A. NEURON OPTIMIZATION OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS FOR STOCK PRICE INDEX PREDICTION. IJEFS. 2013;5(1):12-21.