Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article An Attention-Based Autoencoder Model with Gated Recurrent Unit for Stock Price Movement Prediction(Springer Nature, 2026) Kolukisa, Burak; Bakir-Gungor, Burcu; Akkas, HuseyinPredicting stock movements is crucial for investors looking to maximize their profits in rapidly changing financial markets. However, noise in stock price data makes it harder to detect the trends and ultimately decreases the performance of predictive models. To address these challenges that are caused by noise, this study proposes two novel models, i.e., an Attention-based Autoencoder (ABA) and an Attention-based Variational Autoencoder with Gated Recurrent Units (AGRUA), which uniquely integrate denoising, attention, and GRU layers. Firstly, a data set is created by adding 9 different technical indicators to the historical data of Borsa Istanbul (BIST 30) stocks. Secondly, proposed methods and other well-known deep learning models were used to remove noise from the data sets. Finally, each denoised dataset was fed separately to the Extreme Gradient Boosting model and subjected to a buy-sell process. The results were measured using trading indicators such as amount of the profits and Sharpe Ratio, Sortino Ratio and Maximum Drawdown. The proposed models produced substantial financial gains, with AGRUA achieving the highest total profit and ABA achieving the lowest average Maximum Drawdown, thereby demonstrating superior risk-adjusted performance. Lastly, Friedman and Nemenyi tests confirmed that AGRUA and ABA surpass most of the benchmarks in profit and risk-adjusted returns. The performance of the proposed method demonstrates its value in capturing nonlinear market patterns and improving decision accuracy, emphasizing the need for noise reduction before forecasting.Article Citation - Scopus: 1The Communication Strategies of Ideologically Polarized Civil Society Organizations on Twitter: The Case of Turkey(Springer Nature, 2022) Akboga, Sema; Arik, EnginWe investigated how ideologically polarized civil society organizations (CSOs) in Turkey use Twitter. We analyzed tweets from 20 CSOs in Turkey for a period of 7 months by using the Information-Community-Action framework. For all types of CSOs, the number of information tweets was higher than the number of action tweets, which, in turn, was higher in number than the community tweets. Religious/conservative and anti-government CSOs posted significantly more tweets than secular and pro-government CSOs, respectively. Religious/conservative and pro-government CSOs posted more information and community tweets than secular and anti-government CSOs, respectively. The number of anti-government CSOs’ action tweets was higher than that of pro-government CSOs. We, therefore, propose that the ideological stance of a CSO is a factor affecting the content of its tweets in societies where CSOs are politically polarized. © 2024 Elsevier B.V., All rights reserved.