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BRICS-T Ülkelerinde Adaptif Piyasa Hipotezi: Jeopolitik Risklerin Dinamik Etkileri ve Piyasa Tahmin Edilebilirliği

Year 2025, Volume: 26 Issue: 2, 421 - 441, 30.04.2025

Cengizhan Karaca

https://doi.org/10.37880/cumuiibf.1600181

Abstract

Bu çalışma, BRICS-T ülkelerinin (Brezilya, Rusya, Hindistan, Çin, Güney Afrika ve Türkiye) finansal piyasalarını, Adaptif Piyasa Hipotezi (APH) çerçevesinde analiz ederek piyasa etkinliğinin zamanla değişen dinamiklerini ortaya koymayı amaçlamaktadır. Kasım 1997 - Kasım 2024 dönemine ait aylık borsa endeksi verileri kullanılarak APH, Genelleştirilmiş Spektral (GS) testi, Otomatik Portmanteau (AQ) testi ve Wild-Bootstrap Otomatik Varyans Oranı (WBAVR) testi ile sınanmıştır. Zamana bağlı adaptif davranışları incelemek için kayan pencereler tekniği uygulanmış ve APH’nin farklı dönemlerdeki adaptif piyasa özelliği sergileyip sergilemediği test edilmiştir. Ayrıca, jeopolitik risklerin piyasaların tahmin edilebilirliği üzerindeki etkileri doğrusal olmayan modelleme yapabilen ve makine öğrenimine dayalı bir yöntem olan KRLS (Kernel Regularized Least Squares) ile analiz edilmiştir. Elde edilen bulgulara göre, tüm dönemler baz alındığında hiçbir ülkenin adaptif piyasa özelliği göstermediği tespit edilmiştir. Ancak, kayan pancereler yöntemiyle ele alındığında brezilya ve Rusya 2008-2011 arasında güçlü, Hindistan 2006’da çok sınırlı, Çin 2014 ve 2015’te güçlü, Türkiye 2003,2007,2011’de çok sınırlı; 2023 ve 2024’te güçlü adaptif piyasa özelliği sergilemiştir. Güney Afrika’nın hiçbir dönemde adaptif piyasa özelliği sergilemediği görülmektedir. Ayrıca, KRLS dinamik olmayan sonuçları, Jeopolitik risklerin brezilya borsa tahmin edilebilirliğini artırdığını, diğer tüm ülkelerin borsa tahmin edilebilirliğini azalttığını ortaya çıkarmaktadır. Çalışmada, KRLS dinamik sonuçları ile jeopolitik risklerin marjinal etkilerinin ülke borsaları üzerindeki etkisi ortaya çıkarılmakta ve bu sonuçlara göre politika önerileri sunulmaktadır.

Keywords

Adaptif Piyasa Hipotezi BRICS – T ülkeleri Varyans Oran Testleri KRLS Makine Öğrenimi

Ethical Statement

Bu çalışmanın hazırlanma sürecinde bilimsel ve etik ilkelere uyulduğu ve yararlanılan tüm çalışmaların kaynakçada belirtildiği beyan olunur.

Supporting Institution

Bu araştırmayı desteklemek için dış fon kullanılmamıştır.

Thanks

Yazar(lar), bu makalenin kalitesini önemli ölçüde artıran değerli zamanları, derinlemesine geri bildirimleri ve yapıcı önerileri için editör kuruluna ve hakemlere içtenlikle teşekkür eder.

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Adaptive Market Hypothesis in BRICS-T Countries: Dynamic Effects of Geopolitical Risks and Market Predictability

Year 2025, Volume: 26 Issue: 2, 421 - 441, 30.04.2025

Cengizhan Karaca

https://doi.org/10.37880/cumuiibf.1600181

Abstract

This study aims to reveal the time-varying dynamics of market efficiency by analyzing the financial markets of BRICS-T countries (Brazil, Russia, India, China, South Africa, and Turkey) within the framework of the Adaptive Market Hypothesis (AMH). Using monthly stock market index data for the period November 1997 - November 2024, the APH is tested with the Generalized Spectral (GS) test, Automatic Portmanteau (AQ) test, and Wild-Bootstrap Automatic Variance Ratio (WBAVR) test. To examine time-dependent adaptive behavior, we apply the sliding windows technique and test whether APH exhibits adaptive market characteristics in different periods. Moreover, the effects of geopolitical risks on the predictability of markets are analyzed with KRLS (Kernel Regularized Least Squares), a machine learning-based method that can perform nonlinear modeling. According to the findings, no country is found to exhibit adaptive market characteristics across all periods. However, using the rolling window method, Brazil and Russia exhibited strong adaptive market characteristics between 2008 and 2011; India is very limited in 2006, China is strong in 2014 and 2015, and Turkey is very limited in 2003, 2007, and 2011 and is strong in 2023 and 2024. South Africa does not exhibit adaptive market characteristics in any period. Moreover, KRLS non-dynamic results reveal that geopolitical risks increase the predictability of Brazil’s stock market, while they decrease the predictability of all other countries' stock markets. In this study, the KRLS dynamic results and the marginal effects of geopolitical risks on the stock markets of countries are revealed, and policy recommendations are presented according to these results.

Keywords

Adaptive Market Hypothesis BRICS – T Countries Variance-Ratio Tests KRLS Machine Learning

Ethical Statement

It is declared that scientific and ethical principles have been followed while carrying out and writing this study and that all the sources used have been properly cited

Supporting Institution

The author(s) acknowledge that they received no external funding in support of this research.

Thanks

The author(s) sincerely thank the editorial board and reviewers for their valuable time, insightful feedback, and constructive suggestions, which have significantly contributed to enhancing the quality of this manuscript.

References

  • Adnan, M., Rasheed, M. H., Maqbool, Z., & Tariq, S. (2023). Adaptive Vs Efficient: A Comparative Analysis of Stock and Foreign Exchange Markets in Pakistan. Journal of Asian Development Studies, 12(3), Article 3. https://poverty.com.pk/index.php/Journal/article/view/156
  • Akbar, M., Ullah, I., Ali, S., & Rehman, N. (2024). Adaptive market hypothesis: A comparison of Islamic and conventional stock indices. International Review of Economics & Finance, 89, 460-477. https://doi.org/10.1016/j.iref.2023.06.020
  • Aleknevičienė, V., Klasauskaitė, V., & Aleknevičiūtė, E. (2022). Behavior of calendar anomalies and the adaptive market hypothesis: Evidence from the Baltic stock markets. Journal of Baltic Studies, 53(2), 187-210. https://doi.org/10.1080/01629778.2021.1990094
  • Ayteki̇n, S., & Doğan, S. (2023). Testing the Adaptive Market Hypothesis in Equity Markets in Global Financial Crisis Periods: An Application on Borsa Istanbul Indices. İktisadi İdari ve Siyasal Araştırmalar Dergisi, 8(21), 377-402. https://doi.org/10.25204/iktisad.1208721
  • Bhuyan, B., Patra, S., & Bhuian, R. K. (2020). Market Adaptability and Evolving Predictability of Stock Returns: An Evidence from India. Asia-Pacific Financial Markets, 27(4), 605-619. https://doi.org/10.1007/s10690-020-09308-2
  • Box, G. E. P., & Pierce, D. A. (1970). Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models. Journal of the American Statistical Association, 65(332), 1509-1526. https://doi.org/10.1080/01621459.1970.10481180
  • Burhan, H. A., & Acar, E. (2021). Adaptive Market Hypothesis and Return Predictability: A Hidden Markov Model Application in Borsa Istanbul. Sosyoekonomi, 29(48), Article 48. https://doi.org/10.17233/sosyoekonomi.2021.02.02
  • Caldara, D., & Iacoviello, M. (2022). Measuring Geopolitical Risk. American Economic Review, 112(4), 1194-1225. https://doi.org/10.1257/aer.20191823
  • Charles, A., Darné, O., & Fouilloux, J. (2011). Testing the martingale difference hypothesis in CO2 emission allowances. Economic Modelling, 28(1), 27-35. https://doi.org/10.1016/j.econmod.2010.10.003
  • Charles, A., Darné, O., & Kim, J. H. (2017). Adaptive markets hypothesis for Islamic stock indices: Evidence from Dow Jones size and sector-indices. International Economics, 151, 100-112. https://doi.org/10.1016/j.inteco.2017.05.002
  • Choi, I. (1999). Testing the random walk hypothesis for real exchange rates. Journal of Applied Econometrics, 14(3), 293-308. https://doi.org/10.1002/(SICI)1099-1255(199905/06)14:3<293::AID-JAE503>3.0.CO;2-5
  • Choi, Y., & Lee, S. (2020). The impact of urban physical environments on cooling rates in summer: Focusing on interaction effects with a kernel-based regularized least squares (KRLS) model. Renewable Energy, 149, 523-534. https://doi.org/10.1016/j.renene.2019.12.021
  • Chu, J., Zhang, Y., & Chan, S. (2019). The adaptive market hypothesis in the high frequency cryptocurrency market. International Review of Financial Analysis, 64, 221-231. https://doi.org/10.1016/j.irfa.2019.05.008
  • Ertaş, F. C., & Özkan, O. (2018). Piyasa Etkinliği Açısından Adaptif Piyasa Hipotezi’nin Test Edilmesi: Türkiye ve ABD Hisse Senedi Piyasaları Örneği. Finans Politik ve Ekonomik Yorumlar, 642, Article 642. https://dergipark.org.tr/en/pub/fpeyd/issue/47980/607030
  • Escanciano, J. C., & Lobato, I. N. (2009). An automatic Portmanteau test for serial correlation. Journal of Econometrics, 151(2), 140-149. https://doi.org/10.1016/j.jeconom.2009.03.001
  • Escanciano, J. C., & Velasco, C. (2006). Generalized spectral tests for the martingale difference hypothesis. Journal of Econometrics, 134(1), 151-185. https://doi.org/10.1016/j.jeconom.2005.06.019
  • Eyüboğlu, K., & Eyüboğlu, S. (2020). Borsa İstanbul Endekslerinde Adaptif Piyasa Hipotezinin Geçerliliğinin Test Edilmesi. Yaşar Üniversitesi E-Dergisi, 15(59), Article 59. https://doi.org/10.19168/jyasar.633351
  • Fama, E. F. (1980). Agency Problems and the Theory of the Firm. Journal of Political Economy, 88(2), 288-307. https://doi.org/10.1086/260866
  • Fama, E. F. (1991). Efficient Capital Markets: II. The Journal of Finance, 46(5), 1575-1617. https://doi.org/10.1111/j.1540-6261.1991.tb04636.x
  • Ghazani, M. M., & Ebrahimi, S. B. (2019). Testing the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the crude oil prices. Finance Research Letters, 30, 60-68. https://doi.org/10.1016/j.frl.2019.03.032
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Details

Primary Language Turkish
Subjects Finance, Finance and Investment (Other)
Journal Section Makaleler
Authors

Cengizhan Karaca 0000-0002-8121-7142

Publication Date April 30, 2025
Submission Date December 12, 2024
Acceptance Date February 2, 2025
Published in Issue Year 2025 Volume: 26 Issue: 2

Cite

APA Karaca, C. (2025). BRICS-T Ülkelerinde Adaptif Piyasa Hipotezi: Jeopolitik Risklerin Dinamik Etkileri ve Piyasa Tahmin Edilebilirliği. Cumhuriyet Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 26(2), 421-441. https://doi.org/10.37880/cumuiibf.1600181
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