جستجوی مقالات مرتبط با کلیدواژه "طول پنجره" در نشریات گروه "مالی"

The present study analyzes the prediction of the total index of the Tehran Stock Exchange with the singular spectral analysis method based on the genetic algorithm and overlapping singular spectral analysis. It is practical in terms of purpose and descriptive-analytical in terms of method. Its statistical population is the daily price of the total index of the Tehran Stock Exchange in the ten-year return (2009 to 2018) and the research sample is 2411 data from the logarithmic return of the target index.First, the genetic algorithm was implemented in the SSA method on the index. Then, using the Ov-SSA method in order to improve the reconstruction and resolution of the components, the initial and large time series were divided into small and common consecutive parts and the standard SSA analysis method was used for each part.The results of the research showed that the Ov-SSA analysis method has a higher performance than the GA-SSA analysis method with a lower mean absolute value error.

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.