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

Abstract

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.

Keywords

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

Kaynakça

1. E. F. Fama, Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25, 383-417, 1970. https://doi.org/10.2307/2325486.
2. D. Kahneman and T. Amos, Prospect theory: an analysis of decision under risk. Econometrica, 47, 263-291, 1979. https://doi.org/10.2307/1914185.
3. R. J. Shiller, The volatility of stock market prices. Science, 235(4784), 33–37, 1987. https://doi.org/10.1126/science.235.4784.33.
4. T. Fischer and C. Krauss, Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669, 2018. https://doi.org/10.1016/j.ejor.2017.11.054.
5. J. Devlin, M. W. Chang, K. Lee and K. Toutanova, BERT: pre-training of deep bidirectional transformers for language understanding. arXiv, 1810.04805. https://doi.org/10.48550/arXiv.1810.04805.
6. D. C. Broadstock and G. Filis, Oil price shocks and stock market returns: new evidence from the United States and China. Journal of International Financial Markets, Institutions and Money, Elsevier, 33(C), 417-433, 2014. https://doi.org/10.1016/j.intfin.2014.09.007.
7. K. Du, F. Xing, R. Mao and E. Cambria, Financial sentiment analysis: techniques and applications. ACM Computing Surveys, 56(9), 1-42, 2024. https://doi.org/10.1145/3649451.
8. D. K. Nasiopoulos, K. I. Roumeliotis, D. P. Sakas, K. Toudas and P. Reklitis, Financial sentiment analysis and classification: a comparative study of fine-tuned deep learning models. International Journal of Financial Studies, 13(2), 75, 2025. https://doi.org/10.3390/ijfs13020075.

9. A. Todd, J. Bowden and Y. Moshfeghi, Text-based sentiment analysis in finance: synthesising the existing literature and exploring future directions. Intelligent Systems in Accounting, Finance and Management, 31(1),1549,2024. https://doi.org/10.1002/isaf.1549.
10. N. Huynh, L. De Mello and K. Li, Evolution of investor sentiment: a systematic literature review and bibliometric analysis. International Review of Economics & Finance, Elsevier, 100(C), 2025. https://doi.org/10.1016/j.iref.2025.104115.
11. R. M. Ahadzie, J. P. Owusu and J. K. Woode, The impact of sentiment on realized higher-order moments in the S&P 500: evidence from the fear and greed index. Journal of Risk Financial Management, 18(1), 2, 2025. https://doi.org/10.3390/jrfm18010002.
12. B. O. Kose, Finansal teknolojilerde duygu analizi: pazar, risk ve uyum alanlarında eğilimler ve yaklaşımlar. Business & Management Studies: An International Journal, 13(3), 1279–1292, 2025. https://doi.org/10.15295/bmij.v13i3.2633.
13. N. Fauzan and M. R. Arviansyah, A systematic review on social media sentiment in stock price prediction: data sources, machine learning models, and the evidence on effectiveness. International Conference on Digital Society Society, 2(01), 2025. https://doi.org/10.62201/kk4j5376.
14. M. Malcolm and J. Wurgler, Investor sentiment in the stock market. Journal of Economic Perspectives, 21 (2), 129–152. https://doi.org/10.1257/jep.21.2.129.
15. K. B. Diether, C. J. Malloy and A. Scherbina, Differences of opinion and the cross section of stock returns. The Journal of Finance, 57, 2113-2141. https://doi.org/10.1111/0022-1082.00490.
16. K. Krystyniak, H. Liu and H. Hu, What’s trending? stock-level investor sentiment and returns. International Journal of Financial Studies, 13(3), 158. https://doi.org/10.3390/ijfs13030158.
17. C. Lutz, The asymmetric effects of investor sentiment. Macroeconomic Dynamics, 20(6), 1477-1503, 2016, http://dx.doi.org/10.2139/ssrn.2050435.
18. M. P. Chen, P. F. Chen and C. C. Lee, Asymmetric effects of investor sentiment on industry stock returns: panel data evidence. Emerging Markets Review, 14, 35-54. https://doi.org/10.1016/j.ememar.2012.11.001.
19. U. Uygur and O. Oktay, The impacts of investor sentiment on different economic sectors: evidence from İstanbul stock exchange. Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, 14(4), 236-241. https://doi.org/10.1016/j.bir.2014.08.001.
20. J. Zhenni, G. Kun, S. Yi, L. Lai and L. Zhewen, The industrial asymmetry of the stock price prediction with investor sentiment: based on the comparison of predictive effects with SVR. Journal of Forecasting, 39(7), 1166-1178. https://doi.org/10.1002/for.2681.
21. S. D. Tan and O. Taş, Investor attention and stock returns: evidence from Borsa Istanbul. Borsa Istanbul Review, 19(2), 106–116. https://doi.org/10.1016/j.bir.2018.10.003.