Prediction of Values of Borsa Istanbul Forest, Paper, and Printing Index Using Machine Learning Methods

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dc.authoridPolat, Kinyas/0000-0003-4574-1286
dc.contributor.authorAkyuz, Ilker
dc.contributor.authorPolat, Kinyas
dc.contributor.authorBardak, Selahattin
dc.contributor.authorErsen, Nadir
dc.date.accessioned2025-03-23T19:30:10Z
dc.date.available2025-03-23T19:30:10Z
dc.date.issued2024
dc.departmentSinop Üniversitesi
dc.description.abstractIt 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 studies on determining the macroeconomic factors affecting the stock markets, 10 macroeconomic factors were determined as input. The macroeconomic variables used were crude oil price, exchange rate of USD/TRY, dollar index, BIST100 index, gold price, money supply (M2), S&P 500 index, US 10-year bond interest, export-import coverage rate in the forest products sector, and deposits interest rate. It was determined that all machine learning techniques used in the study performed successfully in predicting the index value, but the k-nearest neighbor algorithm showed the best performance with R-2=0.996, RMSE=71.36, and a MAE of 40.8. Therefore, in line with the current variables, investors can make analyzes using any of the ANN, RF, KNN, and GBM techniques to predict the future index value, which will lead them to accurate results.
dc.identifier.doi10.15376/biores.19.3.5141-5157
dc.identifier.issn1930-2126
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85196716817
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.15376/biores.19.3.5141-5157
dc.identifier.urihttps://hdl.handle.net/11486/5031
dc.identifier.volume19
dc.identifier.wosWOS:001259922400008
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherNorth Carolina State Univ Dept Wood & Paper Sci
dc.relation.ispartofBioresources
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250323
dc.subjectMachine learning
dc.subjectForest industry
dc.subjectIndex prediction
dc.subjectXKAGT
dc.titlePrediction of Values of Borsa Istanbul Forest, Paper, and Printing Index Using Machine Learning Methods
dc.typeArticle

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