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   Comparing Prediction Methods of Artificial Neural Networks in Extracting Financial Cycles of Tehran Stock Exchange Based on Markov Switching and Ant Colony Algorithm  
   
نویسنده Abdollahian Farzaneh ,Mohammad Pourzarandi Mohammad Ebrahim ,Minouei Mehrzad ,Hasheminejad Seyed Mohammad
منبع Iranian Journal Of Finance - 2019 - دوره : 3 - شماره : 2 - صفحه:1 -24
چکیده    The stock exchange is considered to be an important establishment to finance long term projects, on one hand, and to collect savings and finance of private section. the stock exchange can be a safe and secure place to invest surplus funds to purchase corporate stocks. as recession and prosperity in this market can have a great role in stockholders` decisionmaking, it becomes vital to predict these cycles. in this paper, using model msmh(4)ar(2), we extract the financial cycles of the market. then, using the ant colony algorithm, we determine the most significant predictors and predict the market financial cycles using neural networks. the results show that the pnn model performs better in predicting the future market with respect to the criteria of mean squared error, the root mean squared error, the model accuracy and kappa coefficient.
کلیدواژه Market Financial Cycles ,Bear Market ,Bull Market ,Artificial Intelligence ,Markov Switching Model
آدرس Islamic Azad University, Central Tehran Branch, Department Of Industrial Management, Iran, Islamic Azad University, Central Tehran Branch, Faculty Of Management, Department Of Finance, Iran, Islamic Azad University, Central Tehran Branch, Department Of Industrial Management, Iran, Islamic Azad University, Medical Science Branch, Department Of Management, Iran
پست الکترونیکی hasheminejad7@gmail.com
 
     
   
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