Algorithmic Stability in Turbulent Markets: Unveiling the Superiority of Shallow Learning over Deep Architectures in Cryptocurrency Forecasting
Yazarlar (5)
Doç. Dr. Ceyda YERDELEN KAYGIN Kafkas Üniversitesi, Türkiye
Doç. Dr. Musa Gün Recep Tayyip Erdoğan Üniversitesi, Türkiye
Osman Nuri Akarsu
Doç. Dr. Haşim Bağcı Aksaray Üniversitesi, Türkiye
Prof. Dr. Ahmet Yanık Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Mathematics (Q1) | ||
| Dergi ISSN | 2227-7390 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 03-2026 |
| Cilt / Sayı / Sayfa | 14 / 6 / 1–26 | DOI | 10.3390/math14060989 |
| Makale Linki | https://doi.org/10.3390/math14060989 | ||
| UAK Araştırma Alanları |
Finansal Piyasalar ve Kurumlar
Davranışsal Finans
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| Özet |
| Forecasting cryptocurrency prices is challenging due to extreme volatility, nonlinear dynamics, and frequent structural shifts in digital asset markets. While recent research increasingly applies deep learning architectures, the predictive advantage of highly complex models in noisy financial environments remains uncertain. This study evaluates the forecasting performance of shallow and deep learning approaches by comparing Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models, along with hybrid configurations (GRU + SVM, LSTM + SVM, and GRU + LSTM). Using daily data spanning from 1 October 2020 to 23 September 2025 for five major cryptocurrencies—Bitcoin, Ethereum, Binance Coin, Solana, and Ripple—the models are estimated within a consistent framework and assessed using out-of-sample performance metrics, including MAE, MAPE, MSE … |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |