An application on forecasting for stock market prices: hybrid of some metaheuristic algorithms with multivariate adaptive regression splines
Yazarlar (3)
Arş. Gör. Dilek SABANCI
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Bandırma Onyedi Eylül University, Türkiye
Sivas Science And Technology University, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | ESCI dergilerinde yayınlanan tam makale |
| Dergi Adı | International Journal of Intelligent Computing and Cybernetics |
| Dergi ISSN | 1756-378X Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | ESCI |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2023 |
| Cilt No | 16 |
| Sayı | 4 |
| Sayfalar | 847 / 866 |
| DOI Numarası | 10.1108/IJICC-02-2023-0030 |
| Makale Linki | http://dx.doi.org/10.1108/ijicc-02-2023-0030 |
| Özet |
| Purpose: Borsa Istanbul 100 Index, known as BIST100, is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading volume. BIST 100 index prediction is a popular research domain for its complex data structure caused by stock price, commodity, interest rate and exchange rate effects. The study proposed hybrid models using both Genetic, Particle Swarm Optimization, Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction, and MARS for prediction. Design/methodology/approach: This paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock, commodity, interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period (in the process of closing) between January 2020 and June 2021. Findings: The most suitable hybrid model was chosen as PSO & MARS by calculating the RMSE, MSE, GCV, MAE, MAD, MAPE and R2 measurements of training, test and overall dataset to check every model's efficiency. Empirical results demonstrated that the proposed PSO & MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model structure. Originality/value: Using metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex models.ing, test and overall dataset to check every model's efficiency. |
| Anahtar Kelimeler |
| Dimension reduction | Metaheuristic algorithms | MARS | Feature selection | BIST 100 |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 8 |
| SCOPUS | 8 |
| Google Scholar | 15 |