Makroekonomik Göstergelerle BIST100 Endeksinin Tahmini: Makine Öğrenmesi Modelleri ile Karşılaştırmalı Bir Analiz

Öz

Bu çalışmada, çeşitli ekonomik ve finansal göstergelere dayalı olarak BIST100 endeksini tahmin etmek için dört farklı Makine Öğrenmesi (ML) algoritması kullanılmıştır. Tahminler, Python kullanılarak Rastgele Orman (RF), Kategorik Güçlendirme (CatBoost), Gradient Boosting (GB) ve Ridge Regresyonu (RR) algoritmaları kullanılarak oluşturulmuştur. Modelleri değerlendirmek ve karşılaştırmak için Ortalama Mutlak Yüzde Hata (MAPE), Ortalama Karesel Hatanın Kökü (RMSE), Ortalama Mutlak Hata (MAE) ve Belirleme Katsayısı (R2) gibi performans metrikleri kullanılmıştır. Sonuç olarak, MAPE metriğine dayalı en iyi performansı gösteren algoritmanın %3,49'luk bir değerle RF olduğu bulunmuştur. Ayrıca, her algoritma için özellik önem analizleri yapılmış ve değişkenler en etkiliden en az etkiliye doğru sıralanmıştır.

Anahtar Kelimeler

• makine öğrenmesi
• sermaye piyasaları
• BIST 100

Forecasting BIST100 Index with Macroeconomic Indicators: A Comparative Analysis with Machine Learning Models

Öz

In this study, four different Machine Learning (ML) algorithms were used to predict the BIST100 index based on various economic and financial indicators. The predictions were generated using Python for the Random Forest (RF), Categorical Boosting (CatBoost), Gradient Boosting (GB), and Ridge Regression (RR) algorithms. Performance metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to evaluate and compare the models. In conclusion, the best-performing algorithm based on the MAPE metric was found to be RF, with a value of 3.49%. Additionally, feature importance analyses were conducted for each algorithm, and the variables were ranked from the most to the least influential.

Anahtar Kelimeler

• machine learning
• capital markets
• BIST100

Kaynakça

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