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Dergiler / Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi / 2026 / Cilt: 14 Sayı: 2

Jeopolitik Belirsizlik Ortamında TCMB’nin Sözlü Yönlendirme Etkinliği: Nlp ve Var Yaklaşımıyla Bir İnceleme (2018-2026)

The Effectiveness of CBRT Forward Guidance under Geopolitical Uncertainty: An NLP- and VAR-Based Analysis (2018–2026)

Sebahattin Koç(Bitlis Eren Üniversitesi)0000-0002-1911-445X
Sayfa
1539–1577
Yayın Tarihi
2026-08-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Genel olarak TCMB iletişimi, piyasa güvenini güçlendirerek ve anlatı yönetimi yoluyla beklentilerin çıpalanmasını destekleyerek para politikası araçlarını tamamlamaktadır.

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Amaç & Hipotez:Bu çalışma, jeopolitik belirsizlik altında Türkiye Cumhuriyet Merkez Bankasının (TCMB) sözlü yönlendirmesinin etkinliğini ve kurumsal anlatısının makro-finansal istikrara etkilerini incelemektedir.
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Temel Bulgular & Veri:Ocak 2018–Şubat 2026 dönemine ait aylık veriler, FinBERT tabanlı metin analizi ve vektör otoregresyon (VAR) modeliyle analiz edilmiştir.
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Sonuç & Katkı:Genel olarak TCMB iletişimi, piyasa güvenini güçlendirerek ve anlatı yönetimi yoluyla beklentilerin çıpalanmasını destekleyerek para politikası araçlarını tamamlamaktadır.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2018 Verileri
⚙️Değişkenler:TCMB · GPR · NLP

Özet

Bu çalışma, jeopolitik belirsizlik altında Türkiye Cumhuriyet Merkez Bankasının (TCMB) sözlü yönlendirmesinin etkinliğini ve kurumsal anlatısının makro-finansal istikrara etkilerini incelemektedir. Ocak 2018–Şubat 2026 dönemine ait aylık veriler, FinBERT tabanlı metin analizi ve vektör otoregresyon (VAR) modeliyle analiz edilmiştir. TCMB Para Politikası Kurulu (PPK) basın duyuruları sayılaştırılarak bağlamsal duygu kutupluluğunu ölçen Söylem Tonu (NLPSOYLEM) ile normalleştirilmiş risk bağlantılı anahtar kelime yoğunluğunu ölçen İletişim Yoğunluğu (NLPSKOR) göstergeleri oluşturulmuştur. Bu göstergeler, ağırlıklı ortalama fonlama maliyeti, döviz kuru değişimleri ve tüketici enflasyonuyla birlikte VAR sistemine dahil edilmiştir. Logaritması alınmış Jeopolitik Risk Endeksi (LNGPR), sistemin en dışsal değişkeni olarak Cholesky sıralamasında ilk sıraya yerleştirilmiştir. Söylem Tonu, enflasyonun öngörü hatası varyansının %6,15’ini açıklarken ağırlıklı ortalama fonlama maliyetinin payı %0,23’tür. Jeopolitik risk artışlarının ardından iletişim yoğunluğu yükselmekte ve kurumsal ton daha ihtiyatlı hale gelmekte; bu durum eşgüdümlü bir savunmacı iletişim tepkisine işaret etmektedir. İletişim Yoğunluğundaki pozitif bir şok, döviz kurunda istatistiksel olarak anlamlı ve aşağı yönlü bir tepki oluşturmakta, Türk lirasındaki değer kaybı baskısını sınırlamakta ve beklentiler kanalı üzerinden hızlı bir aktarım mekanizmasına işaret etmektedir. Genel olarak TCMB iletişimi, piyasa güvenini güçlendirerek ve anlatı yönetimi yoluyla beklentilerin çıpalanmasını destekleyerek para politikası araçlarını tamamlamaktadır.

Abstract

This study examines the effectiveness of forward guidance by the Central Bank of the Republic of Türkiye (CBRT) and the effects of its institutional narrative on macro-financial stability under geopolitical uncertainty. Monthly data for January 2018–February 2026 are analysed using FinBERT-based text analysis and a vector autoregression (VAR) model. CBRT Monetary Policy Committee (MPC) press releases are quantified to construct Discourse Tone (NLPSOYLEM), capturing contextual sentiment polarity, and Communication Intensity (NLPSKOR), measuring normalised risk-related keyword density. These indicators enter the VAR alongside the weighted average funding cost, exchange-rate changes, and consumer price inflation. The log-transformed Geopolitical Risk Index (LNGPR) is placed first in the Cholesky ordering as the system’s most exogenous variable. Discourse Tone accounts for 6.15% of inflation’s forecast error variance, compared with 0.23% for the weighted average funding cost. Following increases in geopolitical risk, Communication Intensity rises and institutional tone becomes more cautious, indicating a coordinated defensive communication response. A positive Communication Intensity shock produces a statistically significant downward exchange-rate response, limits depreciation pressures on the Turkish lira, and signals rapid transmission through the expectations channel. Overall, CBRT communication complements monetary policy instruments by strengthening market confidence and supporting the anchoring of expectations through narrative governance.

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Bu Makaleye Atıf Yap

Koç, S. (2026). Jeopolitik Belirsizlik Ortamında TCMB’nin Sözlü Yönlendirme Etkinliği: Nlp ve Var Yaklaşımıyla Bir İnceleme (2018-2026). Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, Cilt: 14 Sayı: 2, 1539–1577. https://doi.org/10.18506/anemon.1940619

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Kaynakça (40)

  1. Ahrens, M., Erdemlioglu, D., McMahon, M., Neely, C. J., & Yang, X. (2025). Mind your language: Market responses to central bank speeches. Journal of Econometrics, 249, 105921. https://doi.org/10.1016/j.jeconom.2024.105921
  2. Akarsu, O., Karahan, F., & Torun, H. (2025). Communication as policy and firm uncertainty: Evidence from randomised control trial. In E. Amaral, F. Avalos, & I. Shim (Eds.), Monetary policy decision-making and communication under high uncertainty (BIS Papers No. 163, pp. 121–140). Bank for International Settlements.
  3. Araci, D. T. (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv. https://doi.org/10.48550/arXiv.1908.10063
  4. Ash, E., & Hansen, S. (2023). Text algorithms in economics. Annual Review of Economics, 15, 659–688. https://doi.org/10.1146/annurev-economics-082222-074352
  5. Baird, C. D. (2024). The language of central banking: Probing global monetary policy communication spillovers and central bank shocks with natural language processing tools and a novel text database [Doctoral dissertation, University of Maryland, College Park]. https://doi.org/10.13016/aw41-rfzz
  6. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024
  7. Bennani, H., Fanta, N., Gertler, P., & Horvath, R. (2020). Does central bank communication signal future monetary policy in a (post)-crisis era? The case of the ECB. Journal of International Money and Finance, 104, 102167. https://doi.org/10.1016/j.jimonfin.2020.102167
  8. Bholat, D., Broughton, N., Parker, A., Ter Meer, J., & Walczak, E. (2018). Enhancing central bank communications with behavioural insights (Staff Working Paper No. 750). Bank of England.
  9. Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910–945. https://doi.org/10.1257/jel.46.4.910
  10. Blinder, A. S., Ehrmann, M., de Haan, J., & Jansen, D.-J. (2017). Necessity as the mother of invention: Monetary policy after the crisis. Economic Policy, 32(92), 707–755. https://doi.org/10.1093/epolic/eix013
  11. Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823
  12. Demir, E., & Danisman, G. O. (2021). The impact of economic uncertainty and geopolitical risks on bank credit: Evidence from emerging economies. The North American Journal of Economics and Finance, 57, 101444. https://doi.org/10.1016/j.najef.2021.101444
  13. Emekçi, H. (2025). Central bank communication as a social practice in crisis: AI-driven insights into language, trust, and collective behavior in Türkiye. OPUS Journal of Society Research, Reimagining Social Entanglement, 162–175. https://doi.org/10.26466/opusjsr.1774545
  14. Franconi, A. (2025, April 15). Central banking in times of high geopolitical risk. University of Pavia, Department of Economics. https://doi.org/10.2139/ssrn.4728914
  15. Glas, A., & Müller, L. S. (2021). Talking in a language that everyone can understand? Transparency of speeches by the ECB Executive Board. SSRN. https://doi.org/10.2139/ssrn.3790506
  16. Gorodnichenko, Y., Georgarakos, D., Kenny, G., & Coibion, O. (2025). The impact of geopolitical risk on consumer expectations and spending (NBER Working Paper No. 34195). National Bureau of Economic Research. https://doi.org/10.3386/w34195
  17. Görgün, M. F., & Demireli, E. (2025). Analysis of CBRT monetary policy press releases based on cosine similarity, topic modeling, and sentiment tone. Pamukkale University Journal of Social Sciences Institute, 67, 217–236. https://doi.org/10.30794/pausbed.1611268
  18. Görgün, M. F., Demireli, E., & Güler Özçalık, S. (2025a). Readability in central banks' monetary policy announcements: The cases of the Fed, ECB, and CBRT. Izmir Journal of Management, 6(2), 115–134. https://doi.org/10.56203/iyd.1741719
  19. Görgün, M. F., Demireli, E., & Ural, M. (2025b). Text structure and readability analysis in CBRT communication: A study of monetary policy press releases. International Journal of Social and Economic Studies, 6(1), 110–123. https://doi.org/10.62001/gsijses.1667620
  20. Haldane, A. G. (2018a, May 17). Climbing the public engagement ladder [Speech]. Royal Society for the encouragement of Arts, Manufactures and Commerce (RSA), London, United Kingdom. https://www.bankofengland.co.uk/-/media/boe/files/speech/2018/climbing-the-public-engagement-ladder.pdf
  21. Haldane, A. G. (2018b). Central bank communications: Three lessons [Speech]. University of Edinburgh Business School. Bank of England. https://www.bankofengland.co.uk/speech
  22. Haldane, A., & McMahon, M. (2018). Central bank communications and the general public. AEA Papers and Proceedings, 108, 578–583. https://doi.org/10.1257/pandp.20181082
  23. Hansson, M. (2021). Evolution of topics in central bank speech communication (Working Paper). University of Gothenburg. https://doi.org/10.48550/arXiv.2109.10058
  24. He, Z. (2023). Geopolitical risks and investor sentiment: Causality and TVP-VAR analysis. North American Journal of Economics and Finance, 67, 101947. https://doi.org/10.1016/j.najef.2023.101947
  25. Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research, 40(2), 806–841. https://doi.org/10.1111/1911-3846.12832
  26. Iglesias, J., Ortiz, A., & Rodrigo, T. (2017). How do the emerging markets central bank talk? A big data approach to the Central Bank of Turkey (Working Paper No. 17/24). BBVA Research.
  27. Jarociński, M., & Karadi, P. (2020). Deconstructing monetary policy surprises-The role of information shocks. American Economic Journal: Macroeconomics, 12(2), 1–43. https://doi.org/10.1257/mac.20180090
  28. Jegadeesh, N., & Wu, D. (2015). Deciphering Fedspeak: The information content of FOMC meetings (Working Paper). https://www.aeaweb.org/conference/2016/retrieve.php?pdfid=21466
  29. Koç, S., & Gövdere, B. (2019). The effect of monetary policy on the balance of payments in Turkey: 2003-2015. Bingöl University Journal of Social Sciences Institute, 9(18), 1143–1176. https://doi.org/10.29029/busbed.561899
  30. Koosakul, J., Zhang, L., & Zia, M. (2024). Geopolitical proximity and the use of global currencies (IMF Working Paper No. 24/189). International Monetary Fund. https://doi.org/10.5089/9798400287763.001
  31. Li, X., Chan, S., Zhu, X., Pei, Y., Ma, Z., Liu, X., & Shah, S. (2023). Are ChatGPT and GPT-4 general-purpose solvers for financial text analytics? A study on several typical tasks. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track (pp. 408–422). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-industry.39
  32. Meng, J., Mo, B., & Ding, S. (2025). Central bank communication, economic policy uncertainty, and financial market volatility: A time-varying perspective on Chinese markets. Computational Economics. https://doi.org/10.1007/s10614-025-11080-2
  33. Öztürk Birim, Ş., & Gündüz Cüre, M. (2025). Topic modeling analysis of central bank inflation reports with BERTopic. Manisa Celal Bayar University Journal of Social Sciences, 23(3), 168–194. https://doi.org/10.18026/cbayarsos.1614309
  34. Picault, M., & Renault, T. (2017). Words are not all created equal: A new measure of ECB communication. Journal of International Money and Finance, 79, 136–156. https://doi.org/10.1016/j.jimonfin.2017.09.005
  35. Qi, B., Marie, M., Abdelwahed, A. S., Khatatbeh, I. N., Omran, M., & Fayad, A. A. S. (2023). Bank risk literature (1978–2022): A bibliometric analysis and research front mapping. Sustainability, 15(5), 4508. https://doi.org/10.3390/su15054508
  36. Salachas, E., Kouretas, G. P., & Laopodis, N. T. (2025). Understanding central bank responses to geopolitical risks: Evidence from the Fed and ECB. Journal of Financial Stability, 80, 101452. https://doi.org/10.1016/j.jfs.2025.101452
  37. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
  38. Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., & Mann, G. (2023). BloombergGPT: A large language model for finance. arXiv. https://doi.org/10.48550/arXiv.2303.17564
  39. Jiang, T., & Zeng, Q. (2025). Financial sentiment analysis using FinBERT with application in predicting stock movement (Version 3). arXiv. https://doi.org/10.48550/arXiv.2306.02136
  40. Zhang, J., Chen, S., & Liu, H. (2022). Central bank communication, shadow banking, and bank risk-taking: Theoretical model and PVAR empirical evidence. PLOS ONE, 17(9), e0275110. https://doi.org/10.1371/journal.pone.0275110