University of Isfahan Financial Accounting Research 2322-3405 9 1 2017 05 22 The Comparative Assessment of Data Mining Methods Effectiveness to Forecasting Return and Risk of Stock in Companies Listed in Tehran Stock Exchange The Comparative Assessment of Data Mining Methods Effectiveness to Forecasting Return and Risk of Stock in Companies Listed in Tehran Stock Exchange 57 76 21746 10.22108/far.2017.21746 FA Afsaneh Soroushyar Department of Accounting, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran Mohammad Akhlaghi Department of Accounting, Najaf Abad Branch, Islamic Azad University, Isfahan, Iran Journal Article 2016 03 16 In this study, with using of four algorithms: linear discriminant analysis algorithm, quadratic discriminant analysis algorithm, K-nearest neighbors algorithm and decision tree with the help of 16 independent variables has been addressing to predict stock returns and systematic risk. Four algorithms are running once with using of whole independent variables and one again with using of 4 independent variables that are known with using of filtering approach as the most effectiveness of variables in predicting the return and risk. Then the accuracy of forecasting 4 algorithms in both cases (in total, 8 predictions for return and 8 predictions for risk) compares and chooses the best algorithm. For this purpose data of 107 companies listed in Tehran stock exchange is used during the period of 2002 to 2014. The results show that in the case of using 16 independent variables, the linear discriminant analysis algorithm provides the best prediction for return and the quadratic discriminant analysis algorithm provides the best prediction for systematic risk. But in the case of using independent variables that are chosen, the quadratic discriminant analysis algorithm offers the prediction for return and linear discriminant analysis algorithm offers the best prediction for systematic risk. In general, using of selected independent variables (instead of using whole independent variables), improves the algorithm's ability in prediction of return and systematic risk. In this study, with using of four algorithms: linear discriminant analysis algorithm, quadratic discriminant analysis algorithm, K-nearest neighbors algorithm and decision tree with the help of 16 independent variables has been addressing to predict stock returns and systematic risk. Four algorithms are running once with using of whole independent variables and one again with using of 4 independent variables that are known with using of filtering approach as the most effectiveness of variables in predicting the return and risk. Then the accuracy of forecasting 4 algorithms in both cases (in total, 8 predictions for return and 8 predictions for risk) compares and chooses the best algorithm. For this purpose data of 107 companies listed in Tehran stock exchange is used during the period of 2002 to 2014. The results show that in the case of using 16 independent variables, the linear discriminant analysis algorithm provides the best prediction for return and the quadratic discriminant analysis algorithm provides the best prediction for systematic risk. But in the case of using independent variables that are chosen, the quadratic discriminant analysis algorithm offers the prediction for return and linear discriminant analysis algorithm offers the best prediction for systematic risk. In general, using of selected independent variables (instead of using whole independent variables), improves the algorithm's ability in prediction of return and systematic risk. https://far.ui.ac.ir/article_21746_5915317bfb58a7633851850fcc80b61d.pdf