Comparing Prediction Methods of Artificial Neural Networks in Extracting Financial Cycles of Tehran Stock Exchange based on Markov Switching and Ant Colony Algorithm
Publish place: Iranian Journal of Finance، Vol: 3، Issue: 2
Publish Year: 1398
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJFIFSA-3-2_001
تاریخ نمایه سازی: 24 فروردین 1401
Abstract:
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` decision-making, it becomes vital to predict these cycles. In this paper, using model MSMH(۴)AR(۲), 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.
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Authors
Farzaneh Abdollahian
PhD Candidate, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Mohammad Ebrahim Mohammad Pourzarandi
Prof., Department of Finance, Faculty of Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran.
Mehrzad Minouei
Assistant Prof., Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Seyed Mohammad Hasheminejad
Assistant Prof., Department of Management, Medical Science Branch, Islamic Azad University, Tehran, Iran.
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