Abstract
Stock market share values are affected by different and many forces. It is difficult to exactly predict share values in advance due to the fack that the interactions among these forces are complex. However, short-term estimations of these values are possible with complex deep learning techniques that arise from statistical theories and can be realized with today's computer technologies. In this study, one-day value estimation of ISCTR stock traded in the Borsa Istanbul has been made using long term data. In addition to the data of the related stock, historical data of VAKBN, GARAN, QNBFB and AKBNK stock prices and USD/TRY, BIST30 and BANKX indices were also used in the study in order to increase the estimation ability. In the proposed study, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms in multivariate structure were used with Adam and RMSProp optimizers and 
 their performances were observed.
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