Davood Pirayesh Neghab - Industrial Engineering and Operations Management Koc University Istanbul, Turkey
Arman Hassanniakalager - Adam Smith Business School University of Glasgow Glasgow, United Kingdom

The stock return forecasting is of interest to many investors in financial markets. As the fundamental analysis has more powerful and reliable results than the technical one, the fundamental variables recorded in financialdocuments of a firm are taken into consideration. Present approach utilizes the machine learning algorithms to reach this end. In this paper, three algorithms are to be applied andfitted on data which are a nonlinear regression, tree regression, and stepwise regression. We use the financial records of some stocks in Tehran Stock Exchange (Iran), each stock has 15 feature variables as well as its corresponding return as aresponse variable. The results are compared to each other. Taking into consideration the synchronous validation criterion, the mean absolute error (MAE) for the validation data set isutilized.

regression; forecasting; stock return; machine learning