Yatırımcı duyarlılığının hisse senedi getirileri üzerindeki asimetrik etkisi: BIST 30 endeksi üzerine sektörel derin öğrenme analizi

Öz

Yatırımcı duyarlılığı, özellikle gerçek zamanlı dijital bilgi akışları bağlamında, finansal tahminlerde güçlü bir faktör olarak ortaya çıkmıştır. Bu çalışma, BIST 30 Endeksi'ndeki beş sektörde (Enerji, Havacılık, Savunma, Bankacılık ve Çelik) duyarlılığın asimetrik etkisini incelemektedir. Duyarlılık çıkarma için Türk BERT modelini ve fiyat tahmini için LSTM ağlarını entegre eden hibrit bir derin öğrenme mimarisi kullanarak, duyarlılık ile güçlendirilmiş modellerin geleneksel teknik modellerle karşılaştırıldığında tahmin performansını değerlendiriyoruz. Sonuçlar, sektörler arasında önemli farklılıklar olduğunu göstermektedir: Duygular, Enerji (+14.31%), Havacılık (+3.81%) ve Savunma (+1.58%) sektörlerinde tahmin doğruluğunu artırırken, Bankacılık (-1.50%) ve Çelik (-10.03%) sektörlerinde performansı düşürmektedir. Bu bulgular, duygu analizinin evrensel uygulanabilirliğine meydan okumakta ve bağlama duyarlı, sektöre özgü finansal modelleme ihtiyacını vurgulamakta ve duygunun olay odaklı sektörlerde sinyal görevi gördüğünü, ancak makro odaklı veya emtia bağımlı endüstrilerde gürültü görevi gördüğünü göstermektedir.

Anahtar Kelimeler

• Yatırımcı duyarlılığı
• Hisse senedi Fiyat tahmini
• Derin öğrenme
• Sektörel asimetri

The asymmetric impact of investor sentiment on stock returns: A sectoral deep learning analysis on BIST 30 index

Öz

Investor sentiment has emerged as a powerful factor in financial forecasting, particularly in the context of real-time digital information flows. This study explores the asymmetric effect of sentiment across five sectors in the BIST 30 Index: Energy, Aviation, Defense, Banking, and Steel. Using a hybrid deep learning architecture that integrates a Turkish BERT model for sentiment extraction and LSTM networks for price prediction, we evaluate the predictive performance of sentiment-augmented models versus traditional technical-only models. Results show significant sectoral variation: sentiment improves forecast accuracy in Energy (+14.31%), Aviation (+3.81%), and Defence (+1.58%) sectors, while deteriorating performance in Banking (-1.50%) and Steel (-10.03%). These findings challenge the universal applicability of sentiment analysis and highlight the need for context-aware, sector-specific financial modeling and suggest that sentiment acts as signal in event-driven sectors but as noise in macro-driven or commodity-dependent industries.

Anahtar Kelimeler

• Investor sentiment
• Stock price prediction
• Deep learning
• Sectoral asymmetry

Kaynakça

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