Comparison of intelligence accuracy of data mining algorithms to estimate stocks prices

Forecasting the stock price due to its attractiveness has always been on the focus of experts and capital market activists. In such a way that various prediction models such as technical and fundamental analysis and data mining are increasingly used to predict stock prices. Past studies indicate the possibility of stock price prediction by machine learning models, but the prediction accuracy of these models has not been evaluated. The purpose of this research is to accurately compare the intelligence of five commonly used data mining algorithms, including neural network, Logestic regression, k-nearest neighbors, support vector machines and cross validation. Among the 385 active companies in Tehran Stock Exchange, 72 companies have been selected by the method of systematic elimination and the above models have been implemented to predict stock prices on the daily prices of selected stocks for the years 2009 to 2020. The stock price is used as a dependent variable and changes in the opening price, closing price, highest price, lowest price and volume of trade, daily price of forign currency, gold and oil price are used as independent variables. Three indicators R2, MSE and RMSE are used, to evaluate the accuracy of models, and analysis of variance using F statistics is used to fit the accuracy of the models, and t-student statistic is used to compare two models. The results are showed that among the smart algorithms used, the support vector machine algorithm has the highest accuracy. Matlab software is used to analyze the data and compare the models.