A Hybrid Artificial Neural Network with Metaheuristic Algorithms for Predicting Stock Price

Rahim Ghasemiyeh, Reza Moghdani, Shib Sankar Sana

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


Most investors change stock prices in long-term businesses because of global turbulence in the markets. Consequently, prediction of stock price is a difficult task because of unknown effective factors in this area although previous researches have shown that neural networks are more effective and accurate in many areas than traditional statistical models. The proposed study aims to predict prices on stock exchange via the hybrid artificial neural network models and metaheuristic algorithms which consist of cuckoo search, improved cuckoo search, improved cuckoo search genetic algorithm, genetic algorithm, and particle swarm optimization. The important 28 variables of value-added knowledge related to stock indices are identified as input parameters in this network, and then real values are obtained (http://www.tsetmc.com). The results of the proposed model suggest that particle swarm optimization is a dominant metaheuristic approach to predict stock price according to statistical performances of the above approaches.

Original languageEnglish
Pages (from-to)365-392
Number of pages28
JournalCybernetics and Systems
Issue number4
Early online date13 Mar 2017
Publication statusPublished - 19 May 2017
Externally publishedYes

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