Forecast

Investing in stocks is fraught with long risks that make it tough to manage and predict the choices out there to the investor. Artificial Neural Network (ANN) is a popular method which also incorporates technical analysis for making predictions in financial markets. The purpose of this work is an applied study which is conducted using description based on testing as method. The discussion is established on analytical-computational methods. In this research, the documents and statistics of the Tehran Stock Exchange are used to obtain the desired variables. Descriptive statistics and inferential statistics, as well as Perceptron multi-layer neural networks are utilized to analyze the data of this research. The results of this research show the confirmation of the high prediction accuracy of the Tehran Stock Exchange index compared to other estimation methods by the presented model, which has the ability to predict the total index with less than 1.7% error.

Stock prices are influenced by economic, technological, psychological and geopolitical factors. A review of the literature in this field shows that stochastic approaches, trend analysis and econometrics have been used to demonstrate stock market dynamics and price forecasting. However, these techniques cannot provide a comprehensive overview of market dynamics. Because they ignore the temporal relationship between these factors and are unable to understand their cumulative effects on prices. By integrating chaos theory and continuous data mining based on price acceleration, this study has eliminated these gaps by inventing a new price forecasting method called dynamic stock market recognition simulator and combining two methods: one is delay structures. Or gives time intervals to the data set, and the other is the method of selecting new variables for the market environment. The results showed that the method used can be used to predict the long-term stock price using a small data set with small dimensions.