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Evaluating Machine Learning Models for Gold Risk Premium Prediction: A Comparative Approach

Year 2025, Volume: 9 Issue: 1, 1 - 27, 30.06.2025

Ahmet Akusta

https://doi.org/10.47140/kusbder.1599320

Abstract

This study aims to investigate the effectiveness of machine learning (ML) models in predicting the Gold Risk Premium (ARP) in Turkish financial markets. The ARP is an important financial indicator that shows the extra return demanded by investors to compensate for the risk of investing in gold. Given the volatility and uncertainty of financial markets in Turkey, accurately estimating the ARP is important for investors and the functioning of financial markets. Using a dataset covering the period between 2004 and 2024, various economic and financial variables are used to model the ARP. The study involved data preprocessing, application of various ML models ranging from simple linear regression to complex ensemble methods, and evaluation and comparison based on performance measures such as MAE, MSE, RMSE, RMSLE and MAPE. Linear Regression, Ridge Regression and Random Forest models performed well in ARP prediction. Comparative analyses based on different performance measures identified the models that best capture the dynamics of gold investments in Turkey and the ARP. The research makes an important contribution to the field of financial forecasting by demonstrating the potential of machine learning models in Gold Risk Premium estimation, allowing a better understanding of gold investment strategies in the volatile conditions of emerging markets.

Keywords

Gold Risk Premium Machine Learning Models Financial Forecasting Financial Markets

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Altın Risk Primini Tahmininde Makine Öğrenme Modellerinin Etkinliğinin Keşfi: Karşılaştırmalı Bir Yaklaşım

Year 2025, Volume: 9 Issue: 1, 1 - 27, 30.06.2025

Ahmet Akusta

https://doi.org/10.47140/kusbder.1599320

Abstract

Bu çalışma, Türkiye finans piyasalarında Altın Risk Primini (ARP) tahmin etmede makine öğrenimi (MÖ) modellerinin etkinliğini araştırmayı amaçlamaktadır. ARP, yatırımcıların altına yatırım yapmanın riskini telafi etmek için talep ettiği ekstra getiriyi gösteren önemli bir finansal göstergedir. Türkiye'deki finansal piyasaların oynaklığı ve belirsizliği göz önüne alındığında, ARP'yi doğru bir şekilde tahmin etmek yatırımcılar ve finansal piyasaların işleyişi için önemlidir. 2004-2024 yılları arasını kapsayan bir veri seti kullanılarak, ARP'yi modellemek için çeşitli ekonomik ve finansal değişkenler kullanılmıştır. Çalışmada, veri ön işleme, basit doğrusal regresyondan karmaşık topluluk yöntemlerine kadar çeşitli MÖ modellerinin uygulanması ve MAE, MSE, RMSE, RMSLE ve MAPE gibi performans ölçütlerine dayalı olarak değerlendirme ve karşılaştırma işlemleri gerçekleştirilmiştir. Doğrusal Regresyon, Ridge Regresyon ve Rastgele Orman modelleri ARP tahmininde iyi bir performans göstermiştir. Farklı performans ölçütlerine dayalı karşılaştırmalı analizler, Türkiye'deki altın yatırımlarının dinamiklerini ve ARP'yi en iyi şekilde yakalayan modelleri belirlemiştir. Araştırma, Altın Risk Primi tahmininde makine öğrenimi modellerinin potansiyelini ortaya koyarak, finansal tahminleme alanına önemli bir katkı sağlamakta, gelişmekte olan piyasaların değişken koşullarında altın yatırım stratejilerinin daha iyi anlaşılmasına olanak tanımaktadır.

Keywords

Altın Risk Primi Makine Öğrenimi Modelleri Finansal Tahmin Finansal Piyasalar

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Details

Primary Language Turkish
Subjects Finance
Journal Section Research Articles
Authors

Ahmet Akusta 0000-0002-5160-3210

Publication Date June 30, 2025
Submission Date December 10, 2024
Acceptance Date June 5, 2025
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Akusta, A. (2025). Altın Risk Primini Tahmininde Makine Öğrenme Modellerinin Etkinliğinin Keşfi: Karşılaştırmalı Bir Yaklaşım. Kırklareli Üniversitesi Sosyal Bilimler Dergisi, 9(1), 1-27. https://doi.org/10.47140/kusbder.1599320
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