Stock Price Prediction Using Machine Learning Methods: An Example of Turkish Banks

Ozcelik, Hilal (2024) Stock Price Prediction Using Machine Learning Methods: An Example of Turkish Banks. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study aims to estimate the stock prices of five major banks in Turkey—Akbank, Garanti, Halk, Is, and Yapı Kredi—that are listed on the Istanbul Stock Exchange. To achieve this, a comprehensive dataset was compiled, including ten years of historical stock prices for these banks, ten years of oil prices, S&P 500 prices, iShares prices, USD/TRY exchange rates, as well as textual data comprising daily notification reports from the respective banks. Utilizing the Long Short-Term Memory (LSTM) method, predictive models were developed under two scenarios: one incorporating all variables, including the textual data, and the other excluding the textual data. The results demonstrate that the LSTM model successfully generated accurate predictions and that the inclusion of textual data positively contributed to the performance of the prediction models.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Basilio, Jorge UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HG Finance > Investment > Stock Exchange
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 15:25
Last Modified:	03 Sep 2025 15:25
URI:	https://norma.ncirl.ie/id/eprint/8762

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