Data mining application with machine learning algorithms to manage interest rate risk
| dc.contributor.author | Koçoğlu, Enes | |
| dc.contributor.author | Ersöz, Filiz | |
| dc.date.accessioned | 2024-09-29T16:29:05Z | |
| dc.date.available | 2024-09-29T16:29:05Z | |
| dc.date.issued | 2022 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description.abstract | In trade, the risks taken may increase the expected income; however, they may also cause large amounts of losses as well. Banks transfer the capital and the deposits they collect from their clients to the individuals or institutions in need of profit, taking certain risks into account. One of the important risks taken in this process of capital transfer is the market's change in interest or profit share rates. If the bank transfers the deposit collected with a certain commitment to the market at a lower rate, it will make a loss. Models for predicting future interest or profit share rates gain importance for preventing this situation. The aim of this study is to determine which variables will be taken into account for the loan interest rate that banks will offer to their customers during the lending process, and to create a machine learning model that can predict the loan interest rate that the bank will offer to its customers by using these variables. Multiple Linear Regression analysis was performed to demonstrate the relationship between the variables selected based on the literature review, expert opinions, and the interest rate. In order to facilitate decision-makers in practice, Random Forests, Decision Trees, KNearest Neighbours (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) algorithms from machine learning algorithms were compared by using the prediction model. Accuracy Rate, Cohen's Kappa, Precision, Sensitivity, and F-Measure measurements were used to compare the algorithms used in the study. According to the analysis results, it was observed that the Random Forest algorithm was more successful on the first model consisting of weekly data. The Decision Tree algorithm succeeded more on the second model consisting of monthly data prediction performance. In the model consisting of weekly data, USD Selling Price, Stock Index (BIST100), and Central Bank Gold Reserve from the Multiple Linear Regression variables were found significant in affecting the interest rate. | en_US |
| dc.identifier.doi | 10.15295/bmij.v10i4.2162 | |
| dc.identifier.endpage | 1564 | en_US |
| dc.identifier.issue | 4 | en_US |
| dc.identifier.startpage | 1545 | en_US |
| dc.identifier.trdizinid | 1169633 | en_US |
| dc.identifier.uri | https://doi.org/10.15295/bmij.v10i4.2162 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/1169633 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/10256 | |
| dc.identifier.volume | 10 | en_US |
| dc.indekslendigikaynak | TR-Dizin | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | Business and Management Studies: An International Journal | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.title | Data mining application with machine learning algorithms to manage interest rate risk | en_US |
| dc.type | Article | en_US |
