Research Article
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Assessment of Hybrid Artificial Neural Networks and Metaheuristics for Stock Market Forecasting

Year 2018, Volume: 27 Issue: 1, 63 - 78, 15.04.2018

Abstract

Even though a number of stock market
forecasting studies are done related with hybrid Artificial Neural Network
(ANN) models, no standard procedures are available in the literature for each
stock. This causes a growing interest in using metaheuristic for the designing
of appropriate ANN architecture. Therefore, this study used ten different
metaheuristics including Ant Lion Optimization (ALO), Bird Swarm Optimization
(BSA), Differential Evolution (DE), Grey Wolf Optimization (GWO), Moth-Flame
Optimization (MFO), Multi-verse Optimizer (MVO), Particle Swarm Optimization
(PSO), Simulated Annealing (SA), Weighted Superposition Attraction (WSA), and
Firefly Algorithm (FFLY) to improve the performance of the ANN models. Proposed
hybrid ANN models lead to significant opportunities to forecast stock market
more effectively. Based upon results of performance measures, we also expect
hybrid ANN models provide a remarkable solution for other forecasting problems.

References

  • Abdul-Kader, H., & Salam, M. A. (2012). Evaluation of differential evolution and particle swarm optimization algorithms at training of neural network for stock prediction. International Arab Journal of e-Technology; 2(3): 145–151 Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications; 36: 5932–5941 Baykasoğlu, A., & Akpinar, Ş. (2015). Weighted superposition attraction (WSA): a swarm intelligence algorithm for optimization problems–part 2: constrained optimization. Applied Soft Computing; 37: 396–415 Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences; 237: 82–117 Briza, A. C., & Naval, P. C. (2011). Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Applied Soft Computing; 11: 1191–1201 Chen, M.- Y., Fan, M.- H., Chen, Y.- L., & Wei, H.- M. (2013). Design of experiments on neural network's parameters optimization for time series forecasting in stock markets. Neural Network World; 4(13): 369–393 Das, S. R., Mishra, D., & Rout, M. (2017). A Survey on impact of bio-inspired computation on stock market prediction. Journal of Engineering Science & Technology Review; 10(3): 104–114 Faris, H., Aljarah, I., & Mirjalili, S. (2016). Training feedforward neural networks using multi-verse optimizer for binary classification problems. Applied Intelligence; 45(2): 322–332 Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Systems with Applications; 44: 320–331 Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2017). Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection. Neural Computing and Applications; doi: 10.1007/s00521- 017-3089-2 Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications; 38(8): 10389–10397 Hadavandi, E., Shavandi, H., & Ghanbari, A. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge- Based Systems; 23(8): 800–808 Hassanin, M. F., Shoeb, A. M., & Hassanien, A. E. (2016). Grey wolf optimizer-based back-propagation neural network algorithm. 12th International Computer Engineering Conference; 213–218 Kazem, A., Sharifi, E., Hussain, F. K., Saberi, M., & Hussain, O. K. (2013). Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied soft computing; 13(2): 947–958 Mei, R. N. S., Sulaiman, M. H., Mustaffa, Z., & Daniyal, H. (2017). Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Applied Soft Computing; 59: 210–222 Meng, X.- B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm: bird swarm algorithm. Journal of Experimental & Theoretical Artificial Intelligence; 28(4): 673–687 Mirjalili, S. (2015).The ant lion optimizer. Advances in Engineering Software; 83: 80– 98 Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems; 89: 228–249 Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature- inspired algorithm for global optimization. Neural Computing and Applications; 27(2): 495–513 Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software; 69: 46–61 Nhu, H. N., Nitsuwat, S., & Sodanil, M. (2013). Prediction of stock price using an adaptive neuro-fuzzy inference system trained by firefly algorithm. International Computer Science and Engineering Conference; 302–307 Perez-Llamas, C., & Lopez-Bigas, N. (2011). Gitools: Analysis and visualisation of genomic data using interactive heat-maps, PLoS ONE; 6(5): doi: 10.1371/journal.pone.0019541 Preethi, G., & Santhi, B. (2012). Stock market forecasting techniques: A survey. Journal of Theoretical & Applied Information Technology; 46(1): 24–30 Qiu, M., Song, Y., & Akagi, F. (2016). Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market. Chaos, Solitons & Fractals; 85: 1–7 Rather, A. M., Sastry, V. N., & Agarwal, A. (2017). Stock market prediction and Portfolio selection models: a survey. OPSEARCH; 54: 558–579 Rusu, V., & Rusu, C. (2003). Forecasting methods and stock market analysis. Creative Math; 12: 103–110 Talbi, E.-G. (2009). Metaheuristics: from design to implementation. John Wiley & Sons Xiong, T., Bao, Y., & Hu, Z. (2014). Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowledge- Based Systems; 55: 87–100 Yang, X. S. (2010). Firefly algorithm, lévy flights and global optimization. In: M. Bramer, R. Ellis, M. Petridis, (eds) Research and Development in Intelligent Systems XXVI. Springer, London, 2010 Yu, V. F., Lin, S.- W., Lee, W., & Ting, C.- J. (2010). A simulated annealing heuristic for the capacitated location routing problem. Computers & Industrial Engineering; 58(2): 288–299 Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting; 14: 35–62
Year 2018, Volume: 27 Issue: 1, 63 - 78, 15.04.2018

Abstract

References

  • Abdul-Kader, H., & Salam, M. A. (2012). Evaluation of differential evolution and particle swarm optimization algorithms at training of neural network for stock prediction. International Arab Journal of e-Technology; 2(3): 145–151 Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications; 36: 5932–5941 Baykasoğlu, A., & Akpinar, Ş. (2015). Weighted superposition attraction (WSA): a swarm intelligence algorithm for optimization problems–part 2: constrained optimization. Applied Soft Computing; 37: 396–415 Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences; 237: 82–117 Briza, A. C., & Naval, P. C. (2011). Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Applied Soft Computing; 11: 1191–1201 Chen, M.- Y., Fan, M.- H., Chen, Y.- L., & Wei, H.- M. (2013). Design of experiments on neural network's parameters optimization for time series forecasting in stock markets. Neural Network World; 4(13): 369–393 Das, S. R., Mishra, D., & Rout, M. (2017). A Survey on impact of bio-inspired computation on stock market prediction. Journal of Engineering Science & Technology Review; 10(3): 104–114 Faris, H., Aljarah, I., & Mirjalili, S. (2016). Training feedforward neural networks using multi-verse optimizer for binary classification problems. Applied Intelligence; 45(2): 322–332 Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Systems with Applications; 44: 320–331 Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2017). Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection. Neural Computing and Applications; doi: 10.1007/s00521- 017-3089-2 Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications; 38(8): 10389–10397 Hadavandi, E., Shavandi, H., & Ghanbari, A. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge- Based Systems; 23(8): 800–808 Hassanin, M. F., Shoeb, A. M., & Hassanien, A. E. (2016). Grey wolf optimizer-based back-propagation neural network algorithm. 12th International Computer Engineering Conference; 213–218 Kazem, A., Sharifi, E., Hussain, F. K., Saberi, M., & Hussain, O. K. (2013). Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied soft computing; 13(2): 947–958 Mei, R. N. S., Sulaiman, M. H., Mustaffa, Z., & Daniyal, H. (2017). Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Applied Soft Computing; 59: 210–222 Meng, X.- B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm: bird swarm algorithm. Journal of Experimental & Theoretical Artificial Intelligence; 28(4): 673–687 Mirjalili, S. (2015).The ant lion optimizer. Advances in Engineering Software; 83: 80– 98 Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems; 89: 228–249 Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature- inspired algorithm for global optimization. Neural Computing and Applications; 27(2): 495–513 Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software; 69: 46–61 Nhu, H. N., Nitsuwat, S., & Sodanil, M. (2013). Prediction of stock price using an adaptive neuro-fuzzy inference system trained by firefly algorithm. International Computer Science and Engineering Conference; 302–307 Perez-Llamas, C., & Lopez-Bigas, N. (2011). Gitools: Analysis and visualisation of genomic data using interactive heat-maps, PLoS ONE; 6(5): doi: 10.1371/journal.pone.0019541 Preethi, G., & Santhi, B. (2012). Stock market forecasting techniques: A survey. Journal of Theoretical & Applied Information Technology; 46(1): 24–30 Qiu, M., Song, Y., & Akagi, F. (2016). Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market. Chaos, Solitons & Fractals; 85: 1–7 Rather, A. M., Sastry, V. N., & Agarwal, A. (2017). Stock market prediction and Portfolio selection models: a survey. OPSEARCH; 54: 558–579 Rusu, V., & Rusu, C. (2003). Forecasting methods and stock market analysis. Creative Math; 12: 103–110 Talbi, E.-G. (2009). Metaheuristics: from design to implementation. John Wiley & Sons Xiong, T., Bao, Y., & Hu, Z. (2014). Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowledge- Based Systems; 55: 87–100 Yang, X. S. (2010). Firefly algorithm, lévy flights and global optimization. In: M. Bramer, R. Ellis, M. Petridis, (eds) Research and Development in Intelligent Systems XXVI. Springer, London, 2010 Yu, V. F., Lin, S.- W., Lee, W., & Ting, C.- J. (2010). A simulated annealing heuristic for the capacitated location routing problem. Computers & Industrial Engineering; 58(2): 288–299 Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting; 14: 35–62
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Details

Primary Language Turkish
Journal Section Makaleler
Authors

Ayşe Tuğba Dosdoğru This is me

Aslı Boru

Mustafa Göçken

Mehmet Özçalıcı

Tolunay Göçken

Publication Date April 15, 2018
Submission Date February 24, 2018
Published in Issue Year 2018 Volume: 27 Issue: 1

Cite

APA Dosdoğru, A. T., Boru, A., Göçken, M., Özçalıcı, M., et al. (2018). Assessment of Hybrid Artificial Neural Networks and Metaheuristics for Stock Market Forecasting. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 27(1), 63-78.