Prediction of values of Borsa Istanbul Forest, Paper, and Printing Index using machine learning methods
Yazarlar (4)
Dr. Öğr. Üyesi İLker Akyüz Karadeniz Technical University, Türkiye
Prof. Dr. Kinyas POLAT Sinop Üniversitesi, Türkiye
Prof. Dr. Selahattin BARDAK Sinop Üniversitesi, Türkiye
Doç. Dr. Nadir Ersen Artvin Coruh University, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Bioresources (Q2) | ||
| Dergi ISSN | 1930-2126 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2024 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 13-06-2024 |
| Cilt / Sayı / Sayfa | 19 / 3 / 5141–5157 | DOI | 10.15376/biores.19.3.5141-5157 |
| Makale Linki | https://doi.org/10.15376/biores.19.3.5141-5157 | ||
| UAK Araştırma Alanları |
Fiziksel Kimya
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| Özet |
| It is difficult to predict index values or stock prices with a single financial formula. They are affected by many factors, such as political conditions, global economy, unexpected events, market anomalies, and the characteristics of the relevant companies, and many computer science techniques are being used to make more accurate predictions about them. This study aimed to predict the values of the XKAGT index by using the monthly closing values of the Borsa Istanbul (BIST) Forestry, Paper and Printing (XKAGT) index between 2002 and 2023, and the machine learning techniques artificial neural networks (ANN), random forest (RF), k-nearest neighbor (KNN), and gradient boosting machine (GBM). Furthermore, the performances of four machine learning techniques were compared. Factors affecting stock prices are generally classified as macroeconomic and microeconomic factors. As a result of examining the … |
| Anahtar Kelimeler |
| Forest industry | Index prediction | Machine learning | XKAGT |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 4 |
| Scopus | 3 |
| Google Scholar | 7 |