Stock price forecasting using a hybrid model based on recurring neural network and ANFIS and fuzzy expert system

Subject Areas : Financial engineering

Mostafa Yousofi Tezerjan ¹ , Azam dokht Safi Samghabadi ² , Azizollah Memariani ³

1 - Department of Industrial Enginering, Payame Noor University, Tehran, Iran.
2 - Department of Industrial Enginering, Payame Noor University, Tehran, Iran.
3 - Department of Computer and Electrical Engineering, Kharazmi University, Tehran, Iran.

Received: 2020-04-21 Accepted : 2020-08-04 Published : 2021-03-21

Keywords: technical indicators, Stock Price Forecast, Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Return Neural Network (RNN),

Abstract :

Stock price forecasting is a challenging and attractive topic. Investors are interested in being able to predict the price of different stocks in financial markets. This paper presents a hybrid model that predicts the final stock price for the next day based on the adaptive neuro-fuzzy inference systems (ANFIS) and Return Neural Network (RNN) algorithm using historical data and indicators. Then the results of this model and the status of market rumors enter the fuzzy expert system based on the output of the fuzzy neural system and the return neural network along with the market rumor status and finalize the forecast. The combined model proposed to predict the stock price data of Mobarakeh Steel Company of Isfahan was implemented. In this study, for research data, the data of Tehran Stock Exchange Company related to the stock data of Mobarakeh Steel Company of Isfahan from April 26, 2016 to March 20, 2017 has been used. Four technical indicators used in this study are: Moving Average(MA), Exponential Moving Average(EMA), Relative Strength Index(RSI), and Moving Average Convergence Divergence(MACD). These variables have been used as the input of the adaptive neuro-fuzzy inference systems(ANFIS) to predict the final price of the next day's shares.