Predicting the Overall Index of Tehran Stock Exchange UsingSingular spectrum analysis and Genetic Algorithm

Fluctuations in the financial markets are accompanied by signals and noise. In this paper, in addition to Singular Spectrum Analysis (SSA), a Genetic Algorithm (GA) is used to find the optimal window length and cut-off point, the objective of which is to find the minimum value for the correlation function between signal and noise components. Therefore, first, ten-year data of the overall index of Tehran Stock Exchange during 2009 to 2018 were implemented in three using the SSA method. Then it was solved in the form of an optimization problem by a genetic algorithm. The results of the first hypothesis showed that signal and noise resolution is possible in the SSA method. Also, according to the results of the research, Singular spectrum analysis based on genetic algorithm with an absolute value of less than the average value showed an improvement in prediction accuracy. Finally, considering the lowest weight correlation between time series components for signal and noise separation (finding the cut-off point) and then obtaining the optimal window length in the SSA based on GA, indicates the fact that the amount of parameters can be changed. Improve the performance of the SSA method to be useful.