The objective of this study was to the relationship between the rate of fluctuation scale and index changes in Tehran Stock Exchange using the wavelet model. The method of this research is using data technique. One of the most widely used techniques in financial time series is neural network. Due to the comprehensiveness of this technique and the lack of some assumptions about the data, it has become more widespread compared to statistical data. However, noise in time series, especially in financial and economic time series, reduces the accuracy of neural network (NN) predictions. One method of descaling in time series is wavelet transform. The results showed that with constant inflation rate as a controlling variable of exchange rate fluctuation scales and stock price index, from 2005 to 2016, there is a negative and very coherent coherence in long-term scales. According to the results, it can be said that one of the most fundamental issues in terms of training guidelines is the economic situation and the adoption of investment strategies. Accordingly, this shows that in recent years, the long-term decline in the stock price index has reduced exchange rate fluctuations. The results of this research can be an educational guide for those who want to invest in the stock market.
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