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Dergiler / Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi / 2026 / Cilt: 21 Sayı: 2

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

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

Seda Turnacıgil(TOROS UNIVERSITY)0000-0002-8573-8412
Nur Selin Özen(TOROS UNIVERSITY)0000-0001-8545-8771
Ecem Arık(TOROS UNIVERSITY)0000-0003-1528-9460
Sayfa
536–558
Yayın Tarihi
2026-07-31
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Sonuç olarak, MAPE metriğine dayalı en iyi performansı gösteren algoritmanın %3,49'luk bir değerle RF olduğu bulunmuştur.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Bu çalışmada, çeşitli ekonomik ve finansal göstergelere dayalı olarak BIST100 endeksini tahmin etmek için dört farklı Makine Öğrenmesi (ML) algoritması kullanılmıştır.
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Temel Bulgular & Veri: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.
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Sonuç & Katkı:Sonuç olarak, MAPE metriğine dayalı en iyi performansı gösteren algoritmanın %3,49'luk bir değerle RF olduğu bulunmuştur.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:makine öğrenmesi · sermaye piyasaları · BIST 100

Özet

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.

Abstract

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.

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Bu Makaleye Atıf Yap

Turnacıgil, S., Özen, N. S., Arık, E. (2026). Makroekonomik Göstergelerle BIST100 Endeksinin Tahmini: Makine Öğrenmesi Modelleri ile Karşılaştırmalı Bir Analiz. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, Cilt: 21 Sayı: 2, 536–558. https://doi.org/10.17153/oguiibf.1833547

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