Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini

Ahmet Akusta

doi:10.52642/susbed.1533673

EN TR

Sectoral Data Integration in Machine Learning: Predicting Real Estate Investment Trust (REIT) Stock Prices

Öz

The main objective of this study is to use sectoral data and advanced machine learning models to predict the stock prices of Real Estate Investment Trust (EKGYO). The strong correlations between EKGYO stock prices and macroeconomic indicators reveal the effects of general economic conditions on the financial performance of the real estate sector. In this study, important economic indicators such as USD/TL exchange rate, housing price index, domestic producer price index (D-PPI) and mortgage sales are examined and the relationship between these indicators and EKGYO stock prices is analysed in detail. Empirical findings show that the Kalman Filter model provides the highest prediction accuracy with the lowest mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) values. This reveals the potential of the Kalman Filter model to manage fluctuations in financial data and provide accurate forecasts. Although it has slightly higher error rates compared to the Kalman Filter, the ETS model also performs well. In contrast, the Neural Prophet model, despite its advanced design to capture seasonality and trends, has higher error rates, indicating some limitations in complex financial data sets or short-term forecasting.

Anahtar Kelimeler

Stock Price Prediction, Machine Learning, Feature Engineering, Time Series Analysis, Financial Markets

Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini

Öz

Bu çalışmanın temel amacı, Emlak Konut Gayrimenkul Yatırım Ortaklığı (EKGYO) hisse senedi fiyatlarını tahmin etmek amacıyla sektörel veriler ve gelişmiş makine öğrenimi modellerini kullanmaktır. EKGYO hisse senedi fiyatları ile makroekonomik göstergeler arasındaki güçlü korelasyonlar, genel ekonomik şartların gayrimenkul sektörünün finansal performansı üzerindeki etkilerini gözler önüne sermektedir. Çalışmada, USD/TL kuru, konut fiyat endeksi, yurt içi üretici fiyat endeksi (Yİ-ÜFE) ve ipotekli konut satışları gibi önemli ekonomik göstergeler incelenmiş ve bu göstergeler ile EKGYO hisse senedi fiyatları arasındaki ilişki detaylı bir şekilde analiz edilmiştir. Ampirik bulgular, Kalman Filtresi modelinin en düşük ortalama mutlak hata (MAE), ortalama kare hata (MSE) ve kök ortalama kare hata (RMSE) değerleri ile en yüksek tahmin doğruluğunu sağladığını göstermektedir. Bu durum, Kalman Filtresi modelinin finansal verilerdeki dalgalanmaları yönetebilme ve doğru tahminler sunabilme potansiyelini ortaya koymaktadır. Kalman Filtresi ile karşılaştırıldığında biraz daha yüksek hata oranlarına sahip olmasına rağmen ETS modelinin de iyi bir performans sergilediği görülmüştür. Buna karşın, Neural Prophet modeli, mevsimsellik ve trendleri yakalamaya yönelik gelişmiş tasarımına rağmen, karmaşık finansal veri setlerinde veya kısa vadeli tahminlerde bazı sınırlamaları işaret eden daha yüksek hata oranlarına sahiptir.

Anahtar Kelimeler

Hisse Senedi Fiyat Tahmini, Makine Öğrenmesi, Özellik Mühendisliği, Zaman Serisi Analizi, Finansal Piyasalar

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Uluslararası Finans

Bölüm

Araştırma Makalesi

Yazarlar

Ahmet Akusta ^*
0000-0002-5160-3210
Türkiye

Yayımlanma Tarihi

30 Nisan 2025

Gönderilme Tarihi

15 Ağustos 2024

Kabul Tarihi

23 Aralık 2024

Yayımlandığı Sayı

Yıl 2025 Sayı: 56

DOI

https://doi.org/10.52642/susbed.1533673

IZ

https://izlik.org/JA64BY23JA

APA

Akusta, A. (2025). Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 56, 147-161. https://doi.org/10.52642/susbed.1533673

AMA

1.Akusta A. Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini. SUSBED. 2025;(56):147-161. doi:10.52642/susbed.1533673

Chicago

Akusta, Ahmet. 2025. “Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini”. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy 56: 147-61. https://doi.org/10.52642/susbed.1533673.

EndNote

Akusta A (01 Nisan 2025) Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 56 147–161.

IEEE

[1]A. Akusta, “Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini”, SUSBED, sy 56, ss. 147–161, Nis. 2025, doi: 10.52642/susbed.1533673.

ISNAD

Akusta, Ahmet. “Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini”. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 56 (01 Nisan 2025): 147-161. https://doi.org/10.52642/susbed.1533673.

JAMA

1.Akusta A. Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini. SUSBED. 2025;:147–161.

MLA

Akusta, Ahmet. “Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini”. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy 56, Nisan 2025, ss. 147-61, doi:10.52642/susbed.1533673.

Vancouver

1.Ahmet Akusta. Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini. SUSBED. 01 Nisan 2025;(56):147-61. doi:10.52642/susbed.1533673

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Sectoral Data Integration in Machine Learning: Predicting Real Estate Investment Trust (REIT) Stock Prices

Öz

Anahtar Kelimeler

Makine Öğrenmesinde Sektörel Veri Entegrasyonu: Emlak Gayrimenkul Yatırım Ortaklığı Hisse Senedi Fiyat Tahmini

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

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