Sadegh Ehteshami - Department of Accounting, Kish International Branch, Islamic Azad University-South Tehran, Tehran, Iran
Mohsen Hamidian - Department of Accounting and Economic, South Tehran Branch, Islamic Azad University, Tehran, Iran
Zohreh Hajiha - Young Researcher and Elite club, East Tehran Branch, Islamic Azad University, Tehran, Iran
Serveh Shokrollahi - Department of Accounting and Economic, South Tehran Branch, Islamic Azad University, Tehran, Iran

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 ۱۸۰ firms listed in Tehran stock exchange during ۲۰۰۹-۲۰۱۵. 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.

Stock trend forecasting, Random forest algorithm, Decision tree algorithm