Predict the risk of falling stock prices by using meta-innovative methods (Cumulative particle motion optimization algorithm) and comparison with logistic regression
Subject Areas : Financial engineering
Esfandiar Malekian
1
(
Professor, Faculty of Economics and Administrative Affairs, University of Mazandaran. Babolsar, Iran
)
hossin fakhari
2
(
Associate Professor, Faculty of Economics and Administrative Affairs, University of Mazandaran. Babolsar, Iran
)
jamal ghasemi
3
(
Associate Professor, Faculty of Engineering &Technology, University of Mazandaran. Babolsar, Iran
)
Sarveh Farzad
4
(
Ph.D. Condidate in Accounting . Faculty of Economics and Administrative Affairs, University of Mazandaran.
)
Keywords: Genetic Algorithm, Artificial Neural Network, stock price risk, Cumulative motion algorithm of particles,
Abstract :
The Crash, which indicates how much specific stock prices are at risk of collapse. Accordingly, the purpose of this research is to model the risk of falling stock price of listed companies in Tehran Stock Exchange using a multivariate optimization algorithm for particle cumulative movement and comparing results with logistic regression. For this purpose, a hypothesis was developed for the study of this issue and the data for 106 members of the Tehran Stock Exchange for the period of 2010-2010 were analyzed. First, 14 independent variables were introduced as inputs of the combined genetic algorithm and artificial neural network, which was considered as a feature selection method, and 7 optimal variables were selected. Then, using particle cumulative algorithm and logistic regression, predicted The risk of falling stock prices. The stock price collapse criterion has been used to calculate the risk of falling stock prices. The research findings show that the particle agglomeration algorithm is more likely than traditional logistic regression to predict the risk of falling stock prices. These findings underscore the need for managers to use meta-metric methods for forecasting.
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