It is a difficult problem to predict the one-day next closing price of bank stocks. Because there are many factors affecting stock prices. In this study, using data from 1 January 2016 to 9 May 2019 date and some bank stocks have been tried to predict the closing prices of the next day. The decision tree and multiple regression methods were used in developing the estimation model due to finding linear patterns in stock movements. Two different sets of input variables were used for the models created with these methods. There are 50 indicators consisting of 46 techniques and 4 fundamental indicators in the first input variable set. The second input variable set, as a result of the reduction in technical indicators there are a total of 33 indicators consisting of 29 technical indicators and 4 basic indicators. For these two different input variable sets, the estimation performance of both models was evaluated by the R-2 criteria. When the R-2 results were analyzed, it was seen that the reduction in the technical indicator had a positive effect on the predictive performance of the models.