Kara, YakupBoyacioglu, Melek AcarBaykan, Omer Kaan2020-03-262020-03-2620110957-41741873-6793https://dx.doi.org/10.1016/j.eswa.2010.10.027https://hdl.handle.net/20.500.12395/26736Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%). (C) 2010 Elsevier Ltd. All rights reserved.en10.1016/j.eswa.2010.10.027info:eu-repo/semantics/closedAccessArtificial neural networksSupport vector machinesPredictionStock price indexArticle38553115319Q1WOS:000287419900072Q1