nohu j. eng. sci.

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

2564-6605

Nigde Omer Halisdemir University

10.28948/ngumuh.1839166

Decision Support and Group Support Systems Affective Computing Deep Learning

Karar Desteği ve Grup Destek Sistemleri Duygusal Bilgi İşleme Derin Öğrenme

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

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

https://orcid.org/0000-0002-8445-4629

Özden

Cevher

ÇUKUROVA ÜNİVERSİTESİ

01 26 2026

16 12 09 2025 01 09 2026

2012

Nigde Omer Halisdemir University Journal of Engineering Sciences

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.

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.

Investor sentiment Stock price prediction Deep learning Sectoral asymmetry

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