Based upon results of performance measures, we also expect hybrid ANN models provide a remarkable solution for other forecasting problems.
📌 3 Maddede Bu Makale
🎯
Amaç & Hipotez:Öz 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.
📊
Temel Bulgular & Veri: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.
💡
Sonuç & Katkı:Based upon results of performance measures, we also expect hybrid ANN models provide a remarkable solution for other forecasting problems.
Öz 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.
DOSDOĞRU, A. T., BORU, A., GÖÇKEN, M., ÖZÇALICI, M., GÖÇKEN, T. (2018). Assessment of Hybrid Artificial Neural Networks and Metaheuristics for Stock Market Forecasting. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 27 - Sayı: 1, 63–78. https://ajindex.com/articles/73272
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 Engineeri