Comparison of Stock Index Forecasting Using Hybrid Models Based on Genetic Algorithm and Harmonic Search with Artificial Neural Network

This paper is aimed to compare stock index forecasting using hybrid models based on Genetic Algorithm (GA) and Harmonic Search (HS) with Artificial Neural Network (ANN). The most relevant technical indicators as inputs and the optimal number of neurons in hidden layer of Artificial Neural Network designated by metaheuristics including Genetic Algorithms and Harmony Search. Daily price index of Tehran Stock Exchange from 21 December 2012 to 21 December 2015 applied to predict and test stock index. The accuracy of forecasting of three models including Regular Artificial Neural Network model, hybrid neural networks based on GA and hybrid neural networks based on HS is evaluated by the prediction error. The results show that the accuracy of prediction in Metaheuristics models such as Genetic Algorithms and Harmony Search in the test period is higher than normal Artificial Neural Network. Also prediction by hybrid neural network model based on harmony Search during the test period compared to hybrid Artificial Neural Network model based on Genetic Algorithm is more accurate.