Stock Price Prediction of Turkish Banks Using Machine Learning Methods

Bora Egüz*, Fırat Ersin Çorbacı, Tolga Kaya

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Stock markets are vital part of the economy since they utilize the cap-ital. Investors in stock markets are providing capital with the expectation of positive return on their investment. In order to assess if an investment going to bring positive return, future prices of the stocks must be estimated. Thanks to development in technology and statistics, relatively more advanced estimation methods are developed and machine learning approaches are being integrated for this task. Following study is suggesting a stock price prediction model for Turkish banks using machine learning methods such as multiple linear regression, ridge regression, lasso regression, support vector machines, decision tree models, random forest, XGBoost method based on a wide dataset which is expanded using sliding windows method. After the models trained and tested, it has been observed that the XGBoost algorithm is superior to the other algorithms according to the result of the test errors. Thus the proposed model is convenient for predicting the stock prices of Turkish banks.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
EditorsCengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030855765
Publication statusPublished - 2022
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey
Duration: 24 Aug 202126 Aug 2021

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Machine learning
  • Sliding windows
  • Stock price
  • Turkish banks
  • XGBoost


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