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.Conference Object Citation - Scopus: 3ATGRUVAE: Reducing Noise and Improving Forecasting Performance in Stock Data(Institute of Electrical and Electronics Engineers Inc., 2024-10-26) Akkaş, Huseyin; Kolukisa, Burak; Bakir-Güngör, BurcuNowadays, to maximize their income, investors and researchers try to predict the future prices of stocks in the market using artificial intelligence algorithms. However, noise in stock price fluctuations negatively a ffects t he accuracy of the forecasts. To this end, Attention Based Variational Autoencoders with Gated Recurrent Units (ATGRUVAE) method is developed to remove the noise in stock price fluctuations a nd compared with variational, basic and noise removing autoencoders. Exper-iments are conducted using historical stock prices of well-known companies such as Apple, Google and Amazon and 9 different indicator values derived from these stock prices. The noise cleaned stocks are then trained and tested on Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and Linear Regression (LR) models. The results show that the proposed ATGRUVAE model outperforms all three models and demonstrates its ability to capture complex patterns in stock market data. © 2025 Elsevier B.V., All rights reserved.