Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It should mention that this research has two hypotheses. It aimed at being practical and it is correlation methodology. The research performed in deductive reasoning. Hypotheses analyzed based on collected data from 180 firms listed in Tehran stock exchange during 2009-2015. Results indicated that algorithms are able to forecast negative stock return. However, random forest algorithm is more powerful than decision tree algorithm. In addition, stock return from last three years and selling growth are the main variables of negative stock return forecasting. Manuscript profile