Forecasting BIST100 Index with Neural Network Ensembles

Beyaz K., EFE M. Ö.

11th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, 28 - 30 November 2019, pp.940-944 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.23919/eleco47770.2019.8990659
  • City: Bursa
  • Country: Turkey
  • Page Numbers: pp.940-944
  • Hacettepe University Affiliated: Yes


This paper aims to provide a neural network-based approach to forecast the direction of movement of BIST 100 stock price index and investigates the difficulties of such an implementation. It is observed that a neural network implementation is highly sensitive to selection of features and optimization parameters such as learning rate. A methodology to overcome the difficulties of neural network implementations to financial time series is proposed in the paper. Several feature selection methods are employed to obtain a subset of the features that can be used in the training of any classification algorithm. The difficulties and benefits of using an ensemble of neural networks instead of a single neural network are also studied. Results have shown that the use of neural network ensembles yields promising results.