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MAKİNE ÖĞRENMESİNDE REGRESYON MODELLERİNİN TAHMİN PERFORMANSLARININ KARŞILAŞTIRILMASI: TÜRKİYE ÜRÜN İHTİSAS BORSASI BUĞDAY ENDEKSİ ÜZERİNE BİR UYGULAMA

Year 2023, Volume: 11 Issue: 2, 602 - 623, 31.12.2023

Hasan Arda Burhan

https://doi.org/10.52122/nisantasisbd.1377642

Abstract

Tarımsal emtia fiyatları, maliye ve para politikalarında farklılaşma ve düzenlemelere yol açarak ekonomiler üzerinde önemli etkiler yaratmakta, özellikle gelişmekte olan ülkelerde hanehalkı ekonomilerini ve satın alma gücünü de etkilemektedirler. Bu fiyatlarındaki istikrarsızlık ve değişkenlik bu ekonomiler üzerinde olumsuz etkilere de neden olmaktadır. Öte yandan, emtia piyasalarındaki varlıklar tahvil ve hisse senetleri gibi popüler hale gelmiştir. Bu nedenle ortaya çıkan risk yönetimi, istikrarlı fiyatlama ve işlem maliyetlerini düşürme ihtiyaçları, ülkelerde ticaret borsalarının kurulmasına yol açmıştır. Gelişmekte olan bir ülke olan Türkiye’de kurulan Türkiye Ürün İhtisas Borsası (TÜRİB) aracılığıyla Elektronik Ürün Senedi (ELÜS) ticaretinin yapılabilmesi böylece mümkün hale gelmiştir. Bu çalışmada, farklı makine öğrenmesi regresyon tekniklerinin tahmin performansları, TÜRİB Buğday Endeksi (TRBBGD) tahmininde ABD Doları-Türk Lirası kuru (USD/TRY), Brent ham petrol fiyatları, gecelik faiz oranını içeren bir model ve 01/04/2021-20/02/2023 dönemi günlük verileri kullanılarak değerlendirilmiştir. Gerçek değerlerle karşılaştırma ve çeşitli performans değerlendirme kriterleri kullanılarak yapılan incelemelere göre tüm yöntemlerin başarılı sonuçlar verdiği, öte yandan ağaç tabanlı makine öğrenmesi yöntemlerinin diğer yaklaşımlara kıyasla daha iyi genel performans gösterdiği ifade edilebilmektedir.

Keywords

Emtia Piyasaları Elektronik Ürün Senedi (ELÜS) TÜRİB Makine Öğrenmesi Regresyon

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COMPARISON OF PREDICTION PERFORMANCES OF REGRESSION MODELS IN MACHINE LEARNING: AN APPLICATION ON THE TURKISH MERCANTILE EXCHANGE WHEAT INDEX

Year 2023, Volume: 11 Issue: 2, 602 - 623, 31.12.2023

Hasan Arda Burhan

https://doi.org/10.52122/nisantasisbd.1377642

Abstract

Agricultural commodity prices have significant impacts on economies by leading to changes and regulations in both fiscal and monetary policies. These also have effects on household economies and consumer purchasing power particularly in developing countries. Thereby, instability and variability in these prices constitute adverse effects on these economies. On the other hand, assets of the commodity markets become popular just as bonds and stocks. Because of this growing interest, needs for managing risks, stable prices and lowering transaction costs has led to establishment of the commodity exchanges. In this context, Turkey put the licensed warehousing system into operation by founding the Turkish Mercantile Exchange (TMEX) to operate trades of Electronic Warehouse Receipts (EWRs). In this study, a model including US Dollar-Turkish Lira exchange rate (USD/TRY), Brent crude-oil prices, overnight interest rate and a daily dataset for the 01/04/2021-20/02/2023 period were used to assess several machine learning regression methods in predicting the TMEX Wheat Index (TMXWHT). As verified by comparisons with actual values and considering performance evaluation criteria, all methods yielded successful outcomes, furthermore, tree-based methods revealed better overall performance.

Keywords

Commodity Markets Electronic Warehouse Receipts (EWRs) TMEX Machine Learning Regression

References

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There are 81 citations in total.

Details

Primary Language English
Subjects Human Geography (Other)
Journal Section Research Article
Authors

Hasan Arda Burhan 0000-0003-4043-2652

Publication Date December 31, 2023
Submission Date October 17, 2023
Acceptance Date December 23, 2023
Published in Issue Year 2023 Volume: 11 Issue: 2

Cite

APA Burhan, H. A. (2023). COMPARISON OF PREDICTION PERFORMANCES OF REGRESSION MODELS IN MACHINE LEARNING: AN APPLICATION ON THE TURKISH MERCANTILE EXCHANGE WHEAT INDEX. Nişantaşı Üniversitesi Sosyal Bilimler Dergisi, 11(2), 602-623. https://doi.org/10.52122/nisantasisbd.1377642
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