presenting a model for predicting various types of stock movement trends in water-intensive industries using a decision tree

Stock market activists are trying to find and apply methods so that they can increase the profit of their capital by predicting the future stock price. therefore, it seems necessary that appropriate, correct and scientific-base principles methods are used to determine the future price of stocks for investors. economists use econometric methods for forecasting in most cases. therefore, the aim of this research was to present a new approach in predicting the types of stock movement trends in water-intensive industries using the decision tree approach. the local domain of this research was the companies listed on the tehran stock exchange active in water-intensive industries and the time do-main was during 2016-2020. in the data section, the research was done by collecting the data of the sample companies by referring to the financial statements, explanatory notes and the stock exchange monthly journal; based on the systematic elimination method, 72 companies active in water-intensive industries were selected as a statistical sample. in order to describe and summarize the collected data, descriptive and inferential statistics have been used using the decision tree approach; the results showed that by using the decision tree approach, it is possible to predict the profit, industry and stock price trends in water-intensive industries. the results obtained in this research are consistent with the documents mentioned in the theoretical framework of research and financial literature.