To Forecat the Recession and Prosperity in the Tehran Stock Exchange using Models of MS and NSGA-ANN

The stock exchange is one of the financial instruments of countries around the world. The recession in this market can have important effects, for example reducing liquidity, reducing the profitability of companies admitted to the stock exchange, and reducing economic growth. In this paper, we are looking for extraction and prediction of time cycles in the stock market. Initially, using the total stock index and the MSI (3) AR (2) model, three cycles of recession, medium prosperity and high prosperity are extracted in the stock market. Then the most important predictor variables are determined by using the integration of the NSGA (II) algorithm and the three types of neural network models and predicted the market situation for the next three months. Finally, the performance of three types of multilayer perceptron neural network, radial basis and probable network were compared in terms of feature selection and prediction of future market situation. The results indicate that all three models have acceptable error rates, accuracy, and Kappa coefficients, and the probable network model has lower error rate, more accuracy and kappa coefficient than other models.