Ünlü,K.D.Potas,N.Yılmaz,M.Civil EngineeringIndustrial Engineering2024-07-052024-07-0520210978-303074056-62213-868410.1007/978-3-030-74057-3_52-s2.0-85113755824https://doi.org/10.1007/978-3-030-74057-3_5https://hdl.handle.net/20.500.14411/4025In recent years trends in analyzing and forecasting financial time series moves from classical Box-Jenkins methodology to machine learning algorithms because of the non-linearity and non-stationary of the time series. In this study, we employed a machine learning algorithm called support vector machine to predict the daily price direction of BIST 100 index. In addition, we use random forest algorithm for feature selection and showed that by removing some features from the model, performance of the model increases. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.eninfo:eu-repo/semantics/closedAccessFeature selectionFinancial time seriesISE 100Random forestSupport vector machineConference Object3346