Stock Price Forecasting and Portfolio Selection Through Machine Learning: An Application on BIST Participation 30 Index

Year 2025, Volume: 23 Issue: 3, 99 - 121, 15.10.2025

Ümit Hasan Gözkonan , Mahmut Karğın

https://doi.org/10.11611/yead.1558158

Abstract

This study aims to forecast stock prices of companies listed in the BIST Participation 30 Index using machine learning techniques and construct optimized portfolios based on these forecasts. Two methods, Linear Regression (LR) and Gated Recurrent Unit (GRU), were applied for price forecasting, and the results were used to create equal-weighted and return-weighted portfolios using the Markowitz mean-variance model. The analysis shows that the GRU model significantly outperforms LR in terms of forecast accuracy, leading to more profitable portfolio strategies. The return-weighted portfolio consistently showed higher performance compared to the equal-weighted portfolio and the benchmark index. These findings highlight the effectiveness of machine learning models, particularly deep learning algorithms like GRU, in enhancing investment strategies and portfolio management within the context of portfolio selection. The study provides a framework for future research to explore other indices and machine learning models.

Keywords

Price Forecast , Portfolio Selection , Machine Learning , Deep Learning , Participation Index

Makine Öğrenmesi ile Hisse Senedi Fiyat Tahmini ve Portföy Seçimi: BIST Katılım 30 Endeksi Üzerine Bir Uygulama

Abstract

Bu çalışmanın amacı, BIST İslami 30 Endeksi'nde listelenen şirketlerin hisse senedi fiyatlarını makine öğrenmesi tekniklerini kullanarak tahmin etmek ve bu tahminlere dayalı olarak optimize edilmiş portföyler oluşturmaktır. Fiyat tahmini için Doğrusal Regresyon (LR) ve Geçitli Tekrarlayan Birim (Gated Recurrent Unit) olmak üzere iki yöntem uygulanmış ve sonuçlar Markowitz ortalama-varyans modeli kullanılarak eşit ağırlıklı ve getiri ağırlıklı portföyler oluşturmak için kullanılmıştır. Analiz, GRU modelinin tahmin doğruluğu açısından LR'den önemli ölçüde daha iyi performans gösterdiğini ve daha karlı portföy stratejilerine yol açtığını göstermektedir. Getiri ağırlıklı portföy, eşit ağırlıklı portföye ve piyasa endeksine kıyasla genel olarak daha yüksek performans göstermiştir. Bu bulgular, özellikle GRU gibi derin öğrenme algoritmalarının, portföy seçimi bağlamında yatırım stratejilerini ve portföy yönetimini geliştirmedeki etkinliğini vurgulamaktadır. Çalışma, diğer endeksleri ve makine öğrenimi modellerini keşfetmeye yönelik gelecekteki araştırmalar için bir çerçeve sunmaktadır.

Keywords

Fiyat Tahmini , Portföy Seçimi , Makine Öğrenmesi , Derin Öğrenme , Katılım Endeksi

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