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.
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