Forecasting Direction of BIST 100 Index: An Integrated Machine Learning Approach

Ünlü K. D., Potas N., Yilmaz M.

7th International Symposium on Chaos, Complexity and Leadership, ICCLS 2020, Virtual, Online, 29 - 31 October 2020, pp.33-46 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1007/978-3-030-74057-3_5
  • City: Virtual, Online
  • Page Numbers: pp.33-46
  • Keywords: Feature selection, Financial time series, ISE 100, Random forest, Support vector machine
  • Ankara Haci Bayram Veli University Affiliated: Yes

Abstract

© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.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.