BORSA ENDEKS YÖNÜNÜN AĞAÇ TABANLI TOPLULUK MAKİNE ÖĞRENMESİ YÖNTEMLERİ İLE TAHMİNİ: BİST-100 ÖRNEĞİ.
The establishment of an effective prediction model for the direction of stock market indices is quite challenging due to the complex and non-stationary nature of financial data. Predicting the upward or downward movements of the stock market index, especially in emerging market exchanges where the i...
| Publicado en: | Journal of Social Sciences Institute / Sosyal Bilimler Enstitüsü Dergisi Vol. 13; no. 27; pp. 324 - 336 |
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| Autores principales: | , |
| Formato: | Artículo |
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Bingol University / Rectorate
Spring2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=177139722&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 177139722 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 13096672 EG7L jtl: Journal of Social Sciences Institute / Sosyal Bilimler Enstitüsü Dergisi issn: 13096672 maglogo: N pubinfo: dt: Spring2024 vid: 13 iid: 27 pid: 74773 pub: Bingol University / Rectorate artinfo: ui: 177139722 10.29029/busbed.1391790 ppf: 324 ppct: 12 formats: tig: atl: BORSA ENDEKS YÖNÜNÜN AĞAÇ TABANLI TOPLULUK MAKİNE ÖĞRENMESİ YÖNTEMLERİ İLE TAHMİNİ: BİST-100 ÖRNEĞİ. aug: au: BÜYÜKKÖR, Yasin DOĞAN, Seyyide affil: Dr. Öğr. Üyesi, Karamanoğlu Mehmetbey Üniversitesi, İktisadi ve İdari Bilimler Fakültesi su: Random forest algorithms Decision trees Statistical smoothing Stock price indexes Machine learning sug: subj: Random forest algorithms Decision trees Statistical smoothing Stock price indexes Machine learning keyword: Ensemble Machine Learning Random Forest Stock Market Way Prediction Tecnical Indicators Borsa Yönü Tahmini CatBoost Rassal Orman Teknik Göstergeler Topluluk Makine Öğrenmesi XGBoost Ensemble Machine Learning Random Forest Stock Market Way Prediction Tecnical Indicators Borsa Yönü Tahmini CatBoost Rassal Orman Teknik Göstergeler Topluluk Makine Öğrenmesi XGBoost ab: The establishment of an effective prediction model for the direction of stock market indices is quite challenging due to the complex and non-stationary nature of financial data. Predicting the upward or downward movements of the stock market index, especially in emerging market exchanges where the impacts of external factors and shocks are observed more deeply, is of significant importance to stakeholders such as investors, governments, financial institutions, and creditors, as well as researchers. The aim of this study is to predict the direction of the stock market index with tree-based ensemble Machine Learning (ML) methods. Technical Indicators calculated after applying Exponential Smoothing to daily Opening, Closing, Highest, Lowest and Volume data of three years were considered as input variables of the model. In addition, the input variable space was expanded by increasing the window lengths of the Technical Indicators. In the study, Random Forest, XGBoost and CatBoost methods, which based on Decision Trees, are used as ensemble ML methods. Bayesian Search was employed to determine the optimal parameters. According to the findings of the study, all selected methods demonstrated accuracy rates ranging from 89.7% to 90.4%, and considering other performance evaluation criterias, XGBoost was identified as the best prediction method. pubtype: Academic Journal doctype: Article src: R language: Turkish refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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