Comparative application of particle algorithm and genetic algorithm in predicting long-term and short-term trend of stock returns
Subject Areas : Financial Economics
javad kiae
1
(Human Science Faculty West Tehran Branch Islamic Azad University Tehran Iran)
zahra farshadfar
2
(Human Science Faculty, West Tehran Branch, Islamic Azad University, Tehran, Iran.)
Keywords: Keywords: particle algorithm, genetic algorithm, data leveling, machine learning, artificial intelligence,
Abstract :
Abstract The lack of certainty in the movement of the stock market has made forecasting a challenging task in the field of financial time series forecasting. On the other hand, it is not easy to analyze the time series data of stock prices due to non-linearity and high noise. Therefore, the aim of this research is to predict the long-term and short-term trend of the capital market. To achieve this goal, artificial intelligence algorithms of particles and genetics have been used in a comparative manner. The studied variable is the total stock price index in Tehran Stock Exchange in the period of 2016 to 2021 and on a monthly basis. The data have been reviewed after collection using the smoothing method for holidays, and in order to increase the accuracy of the models, the optimal window length of each algorithm has been calculated. The findings indicate that the genetic algorithm by minimizing the prediction error is a suitable algorithm for predicting the short-term and long-term trend of the total price index compared to the particle algorithm in the studied time period.
