Yeşil Tahvil Endeksinin Tahmini için Yapay Zeka Destekli Makine Öğrenme Yöntemleri: Karşılaştırmalı Bir Analiz

Abstract

Bu çalışmanın temel amacı, yeşil tahvil endeks değerlerini yapay zeka destekli farklı makine öğrenmesi modelleri ile tahmin ederek literatüre katkıda bulunmaktır. Çeşitli kaynaklardan bir araya getirilen, 1 Haziran 2021 ile 29 Nisan 2024 tarihlerini kapsayan veriler, eğitim ve test kümelerine ayrılmış ve her biri için standart ön işlemler gerçekleştirilmiştir. Modelin bağımlı değişkeni, küresel finans piyasalarındaki yeşil tahvillerin performansını izleyen ve çalışma için kapsamlı bir ölçüt görevi gören Küresel S&P Yeşil Tahvil Endeksi'dir. Eğitilen makine öğrenmesi modellerinin (Random Forest, Doğrusal Regresyon, Rasyonel Kuadratik Gauss Süreci Regresyonu (GPR), XGBoost, MLP ve Doğrusal DVM) performansını değerlendirmek ve karşılaştırmak için değerlendirme ölçütleri olarak RMSE, MSE, MAE, MAPE ve R² kullanılmış ve en iyi performans gösteren model Rasyonel Kuadratik GPR modeli olmuştur. SHAP analizinin son bölümü modelin tahminlerini etkileyen başlıca faktörleri ortaya koymaktadır. Modelin DXY (ABD Doları Endeksi), XAU (Spot Altın Fiyatı) ve MSCI (Morgan Stanley Capital International) gibi makroekonomik göstergelere büyük önem verdiği görülmektedir. Bu çalışmanın, literatürde doğrudan karşılaştırılabilir benzer çalışmaların sınırlı olması nedeniyle alana önemli bir katkı sağlayacağı düşünülmektedir.

Keywords

• Yeşil Tahviller
• Makine Öğrenmesi
• Rasyonel Kuadratik Gauss Süreci Regresyonu
• SHAP Analizi
• Doğrusal Olmayan İlişkiler

Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis

Abstract

The main objective of this study is to contribute to the literature by forecasting green bond index with different machine learning models supported by artificial intelligence. The data from 1 June 2021 to 29 April 2024, collected from many sources, was separated into training and test sets, and standard preparation was conducted for each. The model's dependent variable is the Global S&P Green Bond Index, which monitors the performance of green bonds in global financial markets and serves as a comprehensive benchmark for the study. To evaluate and compare the performance of the trained machine learning models (Random Forest, Linear Regression, Rational Quadratic Gaussian Process Regression (GPR), XGBoost, MLP, and Linear SVM), RMSE, MSE, MAE, MAPE, and R² were used as evaluation metrics and the best performing model was Rational Quadratic GPR. The concluding segment of the SHAP analysis reveals the primary factors influencing the model's forecasts. It is evident that the model assigns considerable importance to macroeconomic indicators, including the DXY (US Dollar Index), XAU (Gold Spot Price), and MSCI (Morgan Stanley Capital International). This work is expected to enhance the literature, as studies directly comparable to this research are limited in this field.

Keywords

• Green Bonds
• Machine Learning
• Rational Quadratic Gaussian Process Regression
• SHAP Analysis
• Nonlinear Relationships

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