Forecasting the BIST 100 Index with Support Vector Machines

Abstract

Recent literature shows that statistical learning algorithms are powerful for forecasting financial time series. In this study, we model and forecast the Borsa İstanbul 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