Derin ve Makine Öğrenme Algoritmalarının BIST100 Endeksi Tahmin Performanslarının Değerlendirilmesi

Abstract

Bu çalışma, Borsa İstanbul BIST 100 endeksinin makine öğrenmesi ve derin öğrenme teknikleri kullanılarak tahmin edilme olasılığını araştırmaktadır. Çalışmada bağımlı değişken olarak BIST 100 endeksi kullanılmıştır. Ayrıca gram altın fiyatı, günlük dolar kuru (TL cinsinden), günlük euro kuru (TL cinsinden), BIST işlem hacmi, günlük Brent petrol fiyatları, BIST işlem hacmi, BIST gecelik repo faizleri ve BIST Sanayi Endeksi (XUSIN) verileri bağımsız değişkenler olarak kullanılmıştır. Türkiye Cumhuriyet Merkez Bankası bu değişkenlere ilişkin günlük istatistikleri sağlamaktadır. Bu çalışmada, Rastgele Orman, K-En Yakın Komşular, Çok Katmanlı Algılayıcı, Radyal Taban Fonksiyonu ve Destek Vektör Makinesi dahil olmak üzere çeşitli derin öğrenme tekrarlayan sinir ağları (RNN) ve makine öğrenimi ağ yapılarının BIST 100 endeksini tahmin etme performansı test edilmiş ve karşılaştırılmıştır. Sonuçlar, CNN modelinin en düşük RMSE ve MSE değerleri ve en yüksek R² değeri ile tahmin doğruluğu açısından diğer modellerden daha iyi performans gösterdiğini ortaya koymaktadır. Bu da CNN'in finansal tahmin için sağlam bir model olduğunu göstermektedir. Çalışmanın ilk bölümünde bu bağlamda ilgili literatür özetlenmiş, ardından yöntem ve sonuçlar açıklanmıştır. Daha sonra elde edilen karşılaştırmalı tahmin değerleri sunulmuştur. Son olarak, sonuçlara ilişkin yorumlar ve öneriler sunularak çalışma sonlandırılmıştır.

Keywords

• Makine Öğrenimi
• Derin Öğrenme
• Finansal Tahmin
• BIST100 Endeksi
• CNN Algoritması

Evaluation of BIST100 Index Prediction Performance of Deep and Machine Learning Algorithms

Abstract

This study investigates the possibility of forecasting the Borsa Istanbul BIST 100 index using machine learning and deep learning techniques. The study uses the BIST 100 index as the dependent variable. In addition, gram gold price, daily dollar exchange rate (in TL), daily euro exchange rate (in TL), BIST trading volume, daily Brent oil prices, BIST trading volume, BIST overnight repo rates, and BIST Industrial Index (XUSIN) data are used as independent variables. The Central Bank of the Republic of Turkey provides daily statistics on these variables. The performance of several deep learning recurrent neural networks (RNN) and machine learning network structures—including Random Forest, K-Nearest Neighbors, Multilayer Perceptron, Radial Basis Function, and Support Vector Machine—for predicting the BIST 100 index is tested and compared in this study. The results indicate that the CNN model outperforms the other models in terms of prediction accuracy, with the lowest RMSE and MSE values, and the highest R² value. This suggests that CNN is a robust model for financial forecasting. The relevant literature is summarized in this context in the first portion of the study, after which the methods and results are described. Then the obtained comparative prediction values are presented. Finally, the study is concluded by presenting the interpretations of the results and recommendations.

Keywords

• Machine Learning
• Deep Learning
• Financial Forecasting
• BIST100 Index
• CNN Algorithm

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