YATIRIMCI DİKKATİ, DUYARLILIĞI VE PİYASA ETKİNLİĞİ: BORSA İSTANBUL ÜZERİNE BİR ANALİZ

Öz

Bu çalışma, Borsa İstanbul’un etkinliğini belirleyen faktörleri davranışsal bir bakış açısıyla değerlendirmeyi amaçlamaktadır. Bu doğrultuda, yatırımcı dikkati göstergesi olarak aşırı getiri, anormal işlem hacmi ve geçmiş getiri değişkenleri ele alınırken, yatırımcı duyarlılığının göstergesi olarak VIX endeksi kullanılmıştır. Çalışma kapsamında, 03.01.2017–30.09.2024 dönemine ait günlük veriler analiz edilmiştir. Piyasa etkinliğinin ölçülmesi amacıyla ilk olarak Sample Entropi yöntemi uygulanmış ve XU100 endeksinin Göreceli Bilgisel Etkinlik Endeksi oluşturulmuştur. Elde edilen bulgular, XU100 endeksinin tam etkin yapıya sahip olmadığını, ancak kısmi etkinlik sergilediğini göstermektedir. Bu sonuç, piyasa etkinliğinin statik olmadığını ve piyasa koşulları, ekonomik krizler ve yatırımcı davranışlarındaki değişimlere bağlı olarak dinamik bir yapı gösterdiğini ortaya koymaktadır. Çalışmanın ikinci aşamasında, piyasa etkinliğini etkileyen faktörlerin uzun dönemli ilişkisini belirlemek amacıyla ARDL sınır testi uygulanmıştır. Analiz bulguları, aşırı getirinin XU100 endeksinin etkinliğini uzun dönemde pozitif yönde etkileyen tek değişken olduğunu göstermektedir. Buna karşılık, VIX endeksinin uzun dönemde piyasa etkinliği üzerinde negatif bir etkisi olduğu tespit edilmiştir. Anormal işlem hacmi ve geçmiş getirilerin piyasa etkinliği üzerinde istatistiksel olarak anlamlı bir etkisinin bulunmaması, bu değişkenlerin fiyat oluşum sürecine her zaman bilgi sunmadığını ortaya koymaktadır. Elde edilen sonuçlar, piyasa etkinliğinin yatırımcı davranışları ve duyarlılıklarına bağlı olarak değişkenlik gösterdiğini vurgulamaktadır. Çalışma bulguları, Adaptif Piyasalar Hipotezi’ni desteklemekte ve piyasa etkinliğini artırmaya yönelik düzenleyici politika ve stratejilerin geliştirilmesine katkı sağlamayı amaçlamaktadır.

Anahtar Kelimeler

• Piyasa Etkinliği
• Yatırımcı Dikkati
• Yatırımcı Duyarlılığı
• Sample Entropi
• ARDL Sınır Testi

INVESTOR ATTENTION, SENTIMENT, AND MARKET EFFICIENCY: AN ANALYSIS OF BORSA ISTANBUL

Öz

This study aims to evaluate the factors determining the efficiency of Borsa Istanbul from a behavioral perspective. Accordingly, extreme returns, abnormal trading volume, and past returns are considered indicators of investor attention, while the VIX index is used as a proxy for investor sentiment. The study analyzes daily data covering the period from 03.01.2017 to 30.09.2024. To measure market efficiency, the Sample Entropy method is first applied, and the Relative Informational Efficiency Index of the XU100 index is constructed. The findings indicate that the XU100 index does not exhibit a fully efficient structure but rather demonstrates partial efficiency. This result reveals that market efficiency is not static and has a dynamic structure depending on changes in market conditions, economic crises and investor behavior. In the second stage of the study, ARDL bounds test was applied to determine the long-term relationship between the factors affecting market efficiency. The analysis findings show that extreme returns are the only variable that positively affects the efficiency of the XU100 index in the long term. In contrast, the VIX index negatively affects market efficiency in the long term. The lack of a statistically significant effect of abnormal trading volume and past returns on market efficiency suggests that these variables do not always contribute information to the price formation process. The findings emphasize that market efficiency varies depending on investor behavior and sentiment development of regulatory policies and strategies to increase market efficiency.

Anahtar Kelimeler

• Market Efficiency
• Investor Attention
• Investor Sentiment
• Sample Entropy
• ARDL Bounds Test

Kaynakça

1. Aboody, D., Lehavy, R., & Trueman, B. (2010). Limited attention and the earnings announcement returns of past stock market winners. Review of Accounting Studies, 15(2), 317-344.
2. Aharon, D. Y. (2020). Uncertainty, fear and herding behavior: Evidence from size-ranked portfolios. Journal of Behavioral Finance, 22(3), 320-337, https://doi.org/10.1080/15427560.2020.1774887
3. Akarsu, S., & Süer, Ö. (2022). How investor attention affects stock returns? Some international evidence. Borsa Istanbul Review, 22(3), 616-626. https://doi.org/10.1016/j.bir.2021.09.001
4. Alkan, S. (2023). Multi-scale sample entropy analysis of the Turkish stock market efficiency. Nicel Bilimler Dergisi, 5(1), 51-63.
5. Alkan, S. (2024). Liquidity and market efficiency in Borsa Istanbul. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 42(3), 371-384.
6. Altuntaş, D., & Ersoy, E. (2021). Yatırımcı duyarlılığının BIST pay piyasasına etkisi. Sosyoekonomi, 29(50), 387-412. https://doi.org/10.17233/sosyoekonomi.2021.04.18
7. Altuntaş, M., Kılıç, E., Pazarcı, Ş., & Umut, A. (2022). Borsa İstanbul alt endekslerinde etkin piyasa hipotezinin test edilmesi: Fourier kırılmalı ve doğrusal olmayan birim kök testlerinden kanıtlar. Ekonomi Politika ve Finans Araştırmaları Dergisi, 7(1), 169-185.
8. Alvarez-Ramirez, J., Rodriguez, E. Ve Alvarez, J. (2012). A multiscale entropy approach for market efficiency. International Review of Financial Analysis, 21, 64–69. https://doi.org/10.1016/j.irfa.2011.12.001

9. Aouadi, A., Arouri, M., & Teulon, F. (2013). Investor attention and stock market activity: Evidence from France. Economic Modelling, 35, 674-681. https://doi.org/10.1016/j.econmod.2013.08.034
10. Audrino, F., Sigrist, F., & Ballinari, D. (2020). The impact of sentiment and attention measures on stock market volatility. International Journal of Forecasting, 36(2), 334-357. https://doi.org/10.1016/j.ijforecast.2019.05.010
11. Ballinari, D., Audrino, F., & Sigrist, F. (2022). When does attention matter? The effect of investor attention on stock market volatility around news releases. International Review of Financial Analysis, 82, 1-28. https://doi.org/10.1016/j.irfa.2022.102185
12. Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21(2), 785–818. doi:10.1093/rfs/hhm079
13. Barber, B. M., Odean, T., & Zhu, N. (2009). Systematic noise. Journal of Financial Markets, 12(4), 547–569 https://doi.org/10.1016/j.finmar.2009.03.003
14. Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4. https://doi.org/10.1016/j.econlet.2017.09.013
15. Bariviera, A. F., Guercio, M. B. ve Martinez, L. B. (2014). Informational efficiency in distressed markets: The case of European corporate bonds. The Economic and Social Review, 45(3, Autumn), 349-369.
16. Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society Series B: Statistical Methodology, 37(2), 149-163. https://doi.org/10.1111/j.2517-6161.1975.tb01532.x
17. Buguk, C., & Wade Brorsen, B. (2003). Testing weak-form market efficiency: Evidence from the Istanbul Stock Exchange. International Review of Financial Analysis, 12(5), 579–590. https://doi.org/10.1016/S1057-5219(03)00065-6
18. Chen, T. (2017). Investor attention and global stock returns. Journal of Behavioral Finance, 18(3), 358-372. https://doi.org/10.1080/15427560.2017.1331235
19. Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499 https://doi.org/10.1111/j.1540-6261.2011.01679.x
20. Daver, G., Karacaer, M., & Ünlü, H. (2013). Testing of BIST and TURKDEX: Random walk and market efficiency. International Journal of Economics and Finance Studies, 5(2), 10-22.
21. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057-1072. https://doi.org/10.2307/1912517
22. 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
23. Dimpfl, T., & Jank, S. (2016). Can internet search queries help to predict stock market volatility?. European Financial Management, 22(2), 171-192 https://doi.org/10.1111/eufm.12058
24. Ding, L., Huang, Y., & Pu, X. (2014). Volatility linkage across global equity markets. Global Finance Journal, 25(2), 71-89, https://doi.org/10.1016/j.gfj.2014.06.002
25. Durusu-Ciftci, D., Ispir, M. S., & Kok, D. (2019). Do stock markets follow a random walk? New evidence for an old question. International Review of Economics & Finance, 64, 165-175. https://doi.org/10.1016/j.iref.2019.06.002
26. Dutta, A. (2018). Implied volatility linkages between the US and emerging equity markets: a note. Global Finance Journal, 35, 138-146, https://doi.org/10.1016/j.gfj.2017.09.002
27. El Ouadghiri, I., Erragragui, E., Jaballah, J., & Peillex, J. (2022). Institutional investor attention and stock market volatility and liquidity: International evidence. Applied Economics, 54(42), 4839-4854 https://doi.org/10.1080/00036846.2022.2036689
28. Fama, E. F. (1970). Efficient capital markets. The Journal of Finance, 25(2), 383-417. https://doi.org/10.2307/2325486
29. Fang, X., Jiang, Y., & Qian, Z. (2014). The effects of individual investors' attention on stock returns: Evidence from the ChiNext market. Emerging Markets Finance and Trade, 50(sup3), 158-168, https://doi.org/10.2753/REE1540-496X5003S309
30. Gao, Y., Zhao, K., Wang, C., & Liu, C. (2020). The dynamic relationship between internet attention and stock market liquidity: A thermal optimal path method. Physica A: Statistical Mechanics and its Applications, 550, 124180 https://doi.org/10.1016/j.physa.2020.124180
31. Gozbasi, O., Kucukkaplan, I., & Nazlioglu, S. (2014). Re-examining the Turkish stock market efficiency: Evidence from nonlinear unit root tests. Economic Modelling, 38, 381-384. https://doi.org/10.1016/j.econmod.2014.01.021
32. Guzella, M., Castro, F. H., & Santana, V. D. F. (2023). The effect of investor attention on the efficiency of the Brazilian stock market. Revista Contabilidade & Finanças, 34(93), e1635 https://doi.org/10.1590/1808-057x20231635.en
33. Gül, S., Yıldırım, S., & Hattapoğlu, M. (2024). Yatırımcı duyarlılığı ve BIST100 endeksi arasındaki ilişki: Toda Yamamoto nedensellik uygulaması. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 24(4), 1589-1616, https://doi.org/10.18037/ausbd.1508532
34. Hailu, S. M., & Vural, G. (2020). Testing the weak form market efficiency of Borsa Istanbul: An empirical evidence from Turkish banking sector stocks. Journal of Economics Finance and Accounting, 7(3), 236-249 https://doi.org/10.17261/Pressacademia.2020.1291
35. Han, L., Li, Z., & Yin, L. (2018). Investor attention and stock returns: International evidence. Emerging Markets Finance and Trade, 54(14), 3168-3188. https://doi.org/10.1080/1540496X.2017.1413980
36. Hansen, B. E. (1995). Rethinking the univariate approach to unit root testing: Using covariates to increase power. Econometric Theory, 11(5), 1148-1171, Cambridge University Press. https://doi.org/10.1017/S0266466600009993
37. Hsieh, S. F., Chan, C. Y., & Wang, M. C. (2020). Retail investor attention and herding behavior. Journal of Empirical Finance, 59, 109-132 https://doi.org/10.1016/j.jempfin.2020.09.005
38. Im, K. Lee, J. & Tieslau, M. A. (2014). More powerful unit root tests with non-normal errors. R. C. Sickles & W. C. Horrace (Ed.), Festschrift in Honor of Peter Schmidt Econometric Methods and Applications. New York: Springer. https://doi.org/10.1007/978-1-4899-8008-3_10
39. Im, K. S., & Schmidt, P. (2008). More efficient estimation under non-normality when higher moments do not depend on the regressors, using residual augmented least squares. Journal of Econometrics, 144(1), 219-233. https://doi.org/10.1016/j.jeconom.2008.01.003
40. Kahneman, D., (1973). Attention and effort. Prentice-Hall Inc., Englewood Cliffs.
41. Korkmaz, T., Çevik, E. I., & Kırcı Çevik, N. (2017). Yatırımcı ilgisi ile pay piyasası arasındaki ilişki: BİST-100 endeksi üzerine bir uygulama. Business and Economics Research Journal, 8(2), 203-215. 10.20409/berj.2017.45
42. Kumar, A. S. ve Kamaiah, B. (2014). Wavelet based sample entropy analysis: A new method to test weak form market efficiency. Theoretical and Applied Economics, XXI(8),597, 19-26.
43. Li, J., & Yu, J. (2012). Investor attention, psychological anchors, and stock return predictability. Journal of Financial Economics, 104(2), 401-419. https://doi.org/10.1016/j.jfineco.2011.04.003
44. Li, Z., Liu, J., Liu, X., & Wu, C. (2024). Investor attention and stock price efficiency: Evidence from quasi‐natural experiments in China. Financial Management, 53(1), 175-225. https://doi.org/10.1111/fima.12432
45. Lo, A. W. (2004). The adaptive markets hypothesis. The Journal of Portfolio Management, 30(5), 15-29. DOI:10.3905/jpm.2004.442611
46. Mbanga, C., Darrat, A. F., & Park, J. C. (2019). Investor sentiment and aggregate stock returns: The role of investor attention. Review of Quantitative Finance and Accounting, 53, 397-428. https://doi.org/10.1007/s11156-018-0753-2
47. Münyas, T. (2022). VIX korku endeksi ve gelişmekte olan ülke borsaları üzerine ampirik bir analiz. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 21(43), 1-19. https://doi.org/10.46928/iticusbe.796019
48. Nguyen, H. T., & Pham, M. H. (2021). Does investor attention matter for market anomalies?. Journal of Behavioral and Experimental Finance, 29, 100451. https://doi.org/10.1016/j.jbef.2020.100451
49. Oh, G., Kim, S., Eom C (2007) Market efficiency in foreign exchange markets. Physica A: Statistical Mechanics and its Applications 382(1), 209-212. https://doi.org/10.1016/j.physa.2007.02.032
50. Olbryś, J. ve Majewska, E. (2022). Regularity in stock market indices within turbulence periods: The sample entropy approach. Entropy, 24(7), 921. https://doi.org/10.3390/e24070921
51. Ortiz-Cruz, A., Rodriguez, E., Ibarra-Valdez, C. ve Alvarez-Ramirez, J. (2012). Efficiency of crude oil markets: Evidences from informational entropy analysis. Energy Policy, 41, 365–373. https://doi.org/10.1016/j.enpol.2011.10.057
52. Öner, H., İçellioğlu, C. Ş., & Öner, S. (2018). Volatilite endeksi (Vix) ile gelişmekte olan ülke hisse senedi piyasası endeksleri arasındaki Engel-Granger eş-bütünleşme ve Granger nedensellik analizi. Finansal Araştırmalar ve Çalışmalar Dergisi, 10(18), 110-124. https://doi.org/10.14784/marufacd.460670
53. Peng, L., & Xiong, W. (2006). Investor attention, overconfidence and category learning. Journal of Financial Economics, 80(3), 563-602 https://doi.org/10.1016/j.jfineco.2005.05.003
54. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.https://doi.org/10.1002/jae.616
55. Poshakwale, S. (2002). The random walk hypothesis in the emerging Indian stock market. Journal of Business Finance & Accounting, 29(9&10), 1275–1299. https://doi.org/10.1111/1468-5957.00469
56. Putri, S. G., Husni, T., & Rahim, R. (2023). The impact of investor sentiment on value and growth stocks return. Business and Investment Review, 1(4), 74-86. https://doi.org/10.61292/birev.99
57. Qian, B., & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence, 26, 25-33. https://doi.org/10.1007/s10489-006-0001-7
58. Reis, P. M. N., & Pinho, C. (2021). A reappraisal of the causal relationship between sentiment proxies and stock returns. Journal of Behavioral Finance, 22(4), 420-442. https://doi.org/10.1080/15427560.2020.1792910
59. Richman, J.S., Moorman, J.R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039–H2049, https://doi.org/10.1152/ajpheart.2000.278.6.H2039
60. Sadeghzadeh, K. (2018). Borsanın psikolojik faktörlere duyarlılığı: Oynaklık endeksi (VIX) ve Tüketici Güven Endeksi (TGE) ile BIST 100 endeksi arasındaki ilişkiler. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 19(2), 238-253.
61. Sakınç, Ö. S., & Sakınç, İ. (2024). Borsa Istanbul katılım endekslerinin etkin piyasa hipotezi analizi: Zayıf formda etkinlik incelemesi. Hitit Sosyal Bilimler Dergisi, (ICAFR'23 Özel Sayısı), 42-53. https://doi.org/10.17218/hititsbd.1391325
62. Salifu, M., Peprah, J. A., Sebu, J., & Cantah, W. G. (2024). Openness policies and financial development in Ghana: An ARDL approach. Heliyon, 10(3). DOI: https://doi.org/10.1016/j.heliyon.2024.e25074
63. Sarıtaş, H., & Nazlıoğlu, E. H. (2019). Korku endeksi, hisse senedi piyasası ve döviz kuru ilişkisi: Türkiye için ampirik bir analiz. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 12(4), 542-551. https://doi.org/10.25287/ohuiibf.538592
64. Sarwar, G. (2012). Is VIX an investor fear gauge in BRIC equity markets? Journal of Multinational Financial Management, 22(3), 55–65. https://doi.org/10.1016/j.mulfin.2012.01.003
65. Smales, L. A. (2017). The importance of fear: Investor sentiment and stock market returns. Applied Economics, 49(34), 3395-3421, https://doi.org/10.1080/00036846.2016.1259754
66. Smales, L. A. (2022). Spreading the fear: The central role of CBOE VIX in global stock market uncertainty. Global Finance Journal, 51, 100679, https://doi.org/10.1016/j.gfj.2021.100679
67. Sugisaki, K. ve Ohmori, H. (2008, August). Realtime estimation of the degree of market efficiency using variable weighted Sample Entropy. In 2008 SICE Annual Conference (ss. 1415-1418). IEEE. 10.1109/SICE.2008.4654880
68. Tas, O., & Guleroglu Atac, C. (2019). Testing random walk hypothesis for Istanbul stock exchange. PressAcademia Procedia, 9(1), 48-53.
69. Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41, 17-35. https://doi.org/10.1016/j.jbankfin.2013.12.010
70. Wang, G. J., Xie, C. ve Han, F. (2012). Multi-scale approximate entropy analysis of foreign exchange markets efficiency. Systems Engineering Procedia 3, 201–208. https://doi.org/10.1016/j.sepro.2011.10.030
71. Wang, J. ve Wang, X. (2021). COVID-19 and financial market efficiency: Evidence from an entropy- based analysis. Finance Research Letters. 42, 101888. https://doi.org/10.1016/j.frl.2020.101888
72. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
73. Yuan, Y. (2015). Market-wide attention, trading, and stock returns. Journal of Financial Economics, 116(3), 548-564. https://doi.org/10.1016/j.jfineco.2015.03.006
74. Yurtoğlu, Y., & Süsay, A. (2023). Yatırımcı duyarlılığının pay piyasası yatırımlarına etkisi: Borsa İstanbul'dan kanıtlar. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(3), 881-891, https://doi.org/10.25287/ohuiibf.1291847
75. Zhang, B., & Wang, Y. (2015). Limited attention of individual investors and stock performance: Evidence from the ChiNext market. Economic Modelling, 50, 94-104. https://doi.org/10.1016/j.econmod.2015.06.009
76. Zhang, Y., Chu, G., & Shen, D. (2021). The role of investor attention in predicting stock prices: The long short-term memory networks perspective. Finance Research Letters, 38, 101484. https://doi.org/10.1016/j.frl.2020.101484
77. Zhao, W., & Zhang, J. (2024). Investor attention and stock liquidity in the Chinese market. International Advances in Economic Research, 30(1), 65-82 https://doi.org/10.1007/s11294-024-09885-2