LDF, QDF & ANN-GA based models for stock market surveillance in Tehran's Stock Exchange

In this study, Discriminant Analysis (DA) model and the hybrid model of Genetic Algorithm based on Artificial Neural Network (ANN-GA) are used to estimate manipulation of stock prices in Tehran Stock Exchange. In this study, first by using screening data method, a sample of 345 companies listed in Tehran Stock Exchange were selected and then information about the 'TEDPIX' index, closing price, volatility of closing price and trading volume in the timeframe years 1387 to 1391 were collected. Afterwards the selected companies categorized into manipulated and non-manipulated groups by using duration dependence test, skewness & kurtosis test and run test. Then with scrutiny of the trend of Tedpix's chart and volume chart of the manipulated group, Start of price manipulation is determined. In next step by using Linear Discriminant Function (LDF), Quadratic Discriminant Function (QDF) and Genetic Algorithm based Artificial Neural Network and by using closing price, volatility of closing price and trading volume variables and also using information in range one year before starting manipulation group and in range four years for non-manipulation group, designed models for forecasting manipulation. At the end, the prediction ability of the models was examined. According to the results, the prediction ability of QDF model compared to the LDF model and ANN model is better.