Abstract
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.
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Ünlü, K.D., Potas, N., Yılmaz, M. (2021). Forecasting Direction of BIST 100 Index: An Integrated Machine Learning Approach. In: Erçetin, Ş.Ş., Açıkalın, Ş.N., Vajzović, E. (eds) Chaos, Complexity and Leadership 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-74057-3_5
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DOI: https://doi.org/10.1007/978-3-030-74057-3_5
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