Developing a model for predicting the Tehran Stock Exchange index using a combination of artificial neural network and Markov hidden model
Subject Areas : Journal of Investment Knowledge
Leila
Talaie Kakolaki
1
(Ph.D. Student, Department of Industrial Management, UAE Branch, Islamic Azad University, Dubai, UAE)
Mehdi
Madanchi
2
(Assistant Professor, Department of Financial Management, Electronic Campus, Islamic Azad University Tehran,)
Taghi
Torabi
3
(Associate Professor, Department of Economic, Science and Research branch, Islamic Azad University, Tehran, Iran,)
Farhad
Ghaffari
4
(Associate Professor ,Department of Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Keywords: total stock index forecast, Markov model, artificial neural network model, Hybrid Model,
Abstract :
The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its location is the Tehran Stock Exchange and during the years 2010 to 2020. Findings showed that the prediction error rate with artificial neural network has a higher accuracy than Markov's hidden model. Also, the prediction error of the hybrid model is much lower than the other two models for predicting the total stock index of Tehran Stock Exchange, so it has higher accuracy for forecasting stocks. According to the MAPE index, the hybrid model method could improve the predictive power of the artificial neural network by 0.044% and also improve the predictive power of the hidden Markov model by 0.70%.
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