comparison of artificial neural network method and hidden markov’s model in predicting tehran stock exchange index

The present study, entitled comparison of artificial neural network method and hidden markov model in predicting tehran stock exchange index, was classified as applied, analytical-mathematical research, the local territory of those companies listed on the tehran stock exchange and its time domain is from 2007 to 2017 that in terms of data collection, it is a post-event research, in order to analyze information from statistics and mathematics, the markov model of secret-neural network model has been used. according to the mape index, the artificial neural network method has been able to improve the prediction power by 0.0343% compared to hidden markov’s model. artificial neural networks with the ability to deduce meanings from complex or ambiguous data are used to extract patterns and identify methods that are very complex and difficult for humans and other computer techniques to be aware of. a trained neural network can be considered as an expert in the category of information given to it for analysis. as a result, due to the complexity and heavy calculations, as well as the long computation time and the lack of access of some researchers to advanced models and markov’s secret model is recommended for those who are looking for a simple, fast and reliable method of forecasting using the artificial neural network method to predict the price of stock indices