Browsing by Author "Potas,N."

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Forecasting Direction of Bist 100 Index: an Integrated Machine Learning Approach
(Springer Science and Business Media B.V., 2021) Ünlü,K.D.; Potas,N.; Yılmaz,M.
In 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.
Forecasting the Bist 100 Index With Support Vector Machines
(World Scientific Publishing Co., 2022) Ünlü,K.D.; Potas,N.; Ylmaz,M.
Recent literature shows that statistical learning algorithms are powerful for forecasting financial time series. In this study, we model and forecast the Borsa Istanbul 100 Index by employing the machine learning algorithm, support vector machine. The dataset contains the highest price, lowest price, closing price and volume of the index for the period between July 2020 and June 2021.We utilize three different kernels. The empirical findings show that linear kernel gives the best result with coefficient of determination of 0.91 and root mean square error of 0.0062. The second best is polynomial kernel, and it is followed by radial basis kernel. The study shows that statistical learning algorithms can be thought of as an alternative to classical time series methodology in forecasting financial time series. © 2022 by World Scientific Publishing Europe Ltd.