ASYMMETRIC INFORMATION FLOW BETWEEN THE BIST 100 INDEX AND ITS CONSTITUENT STOCKS

Abstract

This study investigates the strength and direction of information flow between the BIST 100 index and its constituent stocks using the transfer entropy (TE) method. TE is a widely used, model-free, and data-driven approach for measuring directional information transfer and uncovering causal relationships in both linear and nonlinear contexts. Using daily returns, the analysis reveals that the information transfer between the index and its components is asymmetric, with a dominant flow from the stocks to the index. The results clearly show the influence of constituent stocks on the index and the direction and strength of information exchange between them. Furthermore, the net information flow between the BIST 100 index and sectors was found to vary, and the sectors were thus classified into two groups: those significantly influenced by the dynamics of the index and those playing a key role in shaping it. These findings highlight the heterogeneity of information flow between the index and sectors, and each sector contributes to the index dynamics at different levels.

Keywords

• Transfer entropy
• Information flow
• BIST 100 Index

BİST 100 ENDEKSİ VE BİLEŞENLERİ ARASINDAKİ ASİMETRİK BİLGİ AKIŞI

Abstract

Bu çalışmanın amacı, BİST 100 endeksi ile bu endeks içinde yer alan hisse senetleri arasındaki bilgi akışının yönünü ve büyüklüğünü transfer entropi yöntemiyle incelemektir. Transfer Entropisi (TE), zaman serileri arasındaki doğrusal ve doğrusal olmayan bilgi akışlarını, bu akışların yönünü, ilişkinin büyüklüğünü ve nedensellik ilişkilerini tespit edebilen parametrik olmayan bir yöntemdir. 2022-2024 dönemine ait günlük getiriler kullanılarak yapılan analizde, endeks ile bileşenler arasındaki bilgi transferinin asimetrik olduğunu ortaya koymuş ve hisse senetlerinden endekse doğru bilgi akışının baskın olduğunu göstermiştir. Bulgular, endeksi oluşturan hisse senetlerinin endeks üzerindeki etkisini ve endeks ile hisse senetleri arasındaki bilgi alışverişinin yönünü ve ilişkinin derecesini açıkça ortaya koymaktadır. Ayrıca, BIST 100 endeksi ile sektörler arasındaki net bilgi akışının farklılık gösterdiği tespit edilmiş ve sektörler, endeksten büyük ölçüde etkilenenler ve endeks üzerinde belirleyici rol oynayanlar olarak iki gruba ayrılmıştır. Araştırma sonuçları, endeks ile sektörler arasındaki bilgi akışının homojen olmadığını ve her sektörün endeks dinamiklerine farklı düzeylerde katkı sağladığını ortaya koymaktadır.

Keywords

• Transfer Entropisi
• Bilgi Akışı
• BİST 100 Endeksi

References

1. Ardakani, O. M. (2024). Portfolio optimization with transfer entropy constraints. International Review of Financial Analysis, 96, 103644. https://doi.org/10.1016/j.irfa.2024.103644
2. Arslan, S., & Çelik, M. S. (2018). Türkiye’deki Emeklilik Yatırım Fonlarının Performanslarının BIST-100 Endeksinin Performansı ile Karşılaştırılması. İşletme ve İktisat Çalışmaları Dergisi, 6(4), 61-73. https://doi.org/10.32479/iicd.129
3. Assaf, A., Mokni, K., & Youssef, M. (2023). COVID-19 and information flow between cryptocurrencies, and conventional financial assets. The Quarterly Review of Economics and Finance, 89, 73–81. https://doi.org/10.1016/j.qref.2023.02.010
4. Behrendt, S., Dimpfl, T., Peter, F. J., & Zimmermann, D. J. (2019). RTransferEntropy—Quantifying information flow between different time series using effective transfer entropy. SoftwareX, 10, 100265. https://doi.org/10.1016/j.softx.2019.100265
5. Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104(3), 535-559. https://doi.org/10.1016/j.jfineco.2011.12.010
6. Caserini, N. A., & Pagnottoni, P. (2022). Effective transfer entropy to measure information flows in credit markets. Statistical Methods & Applications, 31(4), 729-757. https://doi.org/10.1007/s10260-021-00614-1
7. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531
8. Dimpfl, T., & Peter, F. J. (2013). Using transfer entropy to measure information flows between financial markets. Studies in Nonlinear Dynamics and Econometrics, 17(1), 85-102. https://doi.org/10.1515/snde-2012-0044

9. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. https://doi.org/10.1016/0304-405X(93)90023-5
10. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438. https://doi.org/10.2307/1912791
11. Hong, Y., Liu, Y., & Wang, S. (2009). Granger causality in risk and detection of extreme risk spillover between financial markets. Journal of Econometrics, 150(2), 271-287. https://doi.org/10.1016/j.jeconom.2008.12.013
12. Hung, N. T., Nguyen, L. T. M., & Vo, X. V. (2022). Exchange rate volatility connectedness during Covid-19 outbreak: DECO-GARCH and transfer entropy approaches. Journal of International Financial Markets, Institutions and Money, 81, 101628. https://doi.org/10.1016/j.intfin.2022.101628
13. İlhan, B. (2021). Türkiye’de Kira Sertifikalarının Tahvil, Döviz Kuru ve BIST100 ile Karşılaştırmalı Performans Ölçümü Üzerine Ampirik Bir Çalışma. Elektronik Sosyal Bilimler Dergisi, 20(79), 1574-1585. https://doi.org/10.17755/esosder.864506
14. Jale, J. S., Júnior, S. F., Stošić, T., Stošić, B., & Ferreira, T. A. (2019). Information flow between Ibovespa and constituent companies. Physica A: Statistical Mechanics and its Applications, 516, 233–239. https://doi.org/10.1016/j.physa.2018.09.150
15. Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255-259. https://doi.org/10.1016/0165-1765(80)90024-5
16. Karataş, C., Tüysüz, S., Küçüklerli, K. B., & Ulusoy, V. (2025). Investigation of the relationship between number of tweets and USDTRY exchange rate with wavelet coherence and transfer entropy analysis. Financial Innovation, 11(1), 14. https://doi.org/10.1186/s40854-024-00710-7
17. Korbel, J., Jiang, X., & Zheng, B. (2019). Transfer entropy between communities in complex financial networks. Entropy, 21(11), 1124. https://doi.org/10.3390/e21111124
18. Kwon, O., & Oh, G. (2012). Asymmetric information flow between market index and individual stocks in several stock markets. Europhysics Letters, 97(2), 28007. https://doi.org/10.1209/0295-5075/97/28007
19. Kwon, O., & Yang, J. S. (2008). Information flow between stock indices. Europhysics Letters, 82(6), 68003. https://doi.org/10.1209/0295-5075/82/68003
20. Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47(1), 13-37. https://doi.org/10.1016/B978-0-12-780850-5.50018-6
21. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. https://doi.org/10.2307/2975974
22. Marschinski, R., & Kantz, H. (2002). Analyzing the information flow between financial time series: An improved estimator for transfer entropy. The European Physical Journal B-Condensed Matter and Complex Systems, 30, 275-281. https://doi.org/10.1140/epjb/e2002-00379-2
23. Narayan, S., & Kumar, D. (2024). Unveiling interconnectedness and risk spillover among cryptocurrencies and other asset classes. Global Finance Journal, 62, 101018. https://doi.org/10.1016/j.gfj.2024.101018
24. Oh, G., Oh, T., Kim, H., & Kwon, O. (2014). An information flow among industry sectors in the Korean stock market. Journal of the Korean Physical Society, 65, 2140–2146. https://doi.org/10.3938/jkps.65.2140
25. Sandoval, L., Jr. (2014). Structure of a global network of financial companies based on transfer entropy. Entropy, 16(8), 4443–4482. https://doi.org/10.3390/e16084443
26. Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. https://doi.org/10.1213/ane.0000000000002864
27. Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464. https://doi.org/10.1103/PhysRevLett.85.461
28. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442. https://doi.org/10.2307/2977928
29. Ünal, B., & Eroğlu, Y. (2022). BIST100 endeksi ve dolar kuru arasındaki ilişkinin transfer entropisi ile analizi. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, 13(2), 539-549.
30. Unal, S., & Güleç, M. A. (2022). Hisse Senedi Fonlarında Karşılaştırma Ölçütü Seçiminin Etkinliği. Abant Sosyal Bilimler Dergisi, 22(1), 243-256. https://doi.org/10.11616/asbi.1021330
31. Wang, X., & Hui, X. (2018). Cross-sectoral information transfer in the Chinese stock market around its crash in 2015. Entropy, 20(9), 663. https://doi.org/10.3390/e20090663
32. Yu, X., & Cifuentes-Faura, J. (2024). Information Spillover among Cryptocurrency and Traditional Financial Assets: Evidence from Complex Networks. Physica A: Statistical Mechanics and its Applications, 129903. https://doi.org/10.1016/j.physa.2024.129903
33. Yue, P., Fan, Y., Batten, J. A., & Zhou, W. X. (2020). Information transfer between stock market sectors: A comparison between the USA and China. Entropy, 22(2), 194. https://doi.org/10.3390/e22020194
34. Zhao, Y., Gao, X., Wei, H., Sun, X., & An, S. (2024). Early warning of systemic risk in commodity markets based on transfer entropy networks: Evidence from China. Entropy, 26(7), 549. https://doi.org/10.3390/e26070549