YÜKSEK FREKANSLI İŞLEMLER HİSSE SENEDİ PİYASALARINDA BALON OLUŞUMUNU ETKİLER Mİ? GELİŞMEKTE OLAN HİSSE SENEDİ PİYASASINDAN KANITLAR

Year 2024, Volume: 20 Issue: 3, 675 - 686

Mehmet Sinan Çelik

https://doi.org/10.17130/ijmeb.1447114

Abstract

Bu çalışma, yüksek frekanslı işlemlerin (HFT) nispeten yeni bir olgu olduğu önemli bir gelişmekte
olan piyasa niteliğindeki Türk hisse senedi piyasası Borsa İstanbul (BIST)’da balon oluşumunu etkileyen
faktörleri incelemektedir. HFT, hızlı algoritmalar kullanılarak gerçekleştirilen işlemleri ifade eder ve
günümüzde finans piyasalarının önemli bir dinamiği haline gelmiştir. Çalışma, 11 Mart 2020 ile 31
Aralık 2020 tarihleri arasındaki döneme ait güniçi ve günlük hisse senedi fiyat verilerini kullanmaktadır.
Veriler, Borsa İstanbul’dan elde edilmiş olup, HFT faaliyetleri, “gün içi emir” verileri ile tespit edilmiştir.
Spekülatif balonların varlığı ise Supremum Augmented Dickey-Fuller (SADF) ve Generalized Sup
Augmented Dickey-Fuller (GSADF) modelleri kullanılarak test edilmiştir. Çalışma, HFT işlemlerinin
balon oluşumunda önemli bir rol oynadığını tespit etmiştir. HFT işlemleri, yüksek işlem hacimleri ve hızlı
işlem yetenekleriyle piyasada aşırı oynaklık ve manipülasyon yaratabilir. Bu durum, balon oluşumunun riskini artırabilir. Çalışma, finansal piyasalardaki HFT etkilerini hafifletmek için düzenleme ve denetimin
önemini vurgulamaktadır. Piyasada şeffaflığı artırmayı amaçlayan düzenlemeler, yatırımcıların daha
bilinçli kararlar almasına yardımcı olabilir.

Keywords

Algoritmik İşlemler, Yüksek Frekanslı İşlemler, Oynaklık, Likidite, Spekülatif Balonlar

DO HIGH-FREQUENCY TRADING AFFECT BUBBLE FORMATION IN STOCK MARKETS? EVIDENCE FROM EMERGING STOCK MARKET

Abstract

This study examines the factors affecting bubble formation in the Turkish stock market Borsa
Istanbul (BIST), an important emerging market where high-frequency trading (HFT) is a relatively new
phenomenon. HFT refers to trades executed using fast algorithms and has become an essential dynamic
of financial markets today. The study uses intraday and daily stock price data between 11 March 2020
and 31 December 2020. The data are obtained from Borsa Istanbul and HFT activities are identified
with ‘intraday order’ data. The existence of speculative bubbles is tested using Supremum Augmented
Dickey-Fuller (SADF) and Generalised Sup Augmented Dickey-Fuller (GSADF) models. The study finds
that HFT transactions play an important role in bubble formation. With their high trading volumes and
fast trading capabilities, HFT trades can create excessive volatility and manipulation in the market.
This may increase the risk of bubble formation. The study emphasises the importance of regulation and
supervision in mitigating HFT effects in financial markets. Regulations aimed at increasing transparency
in the market can help investors make more informed decisions.

Keywords

Algorithmic Trading, High-Frequency Trading, volatiliy, liquidity, speculative bubbles

References

• Ammar, I.B., Hellara, S., & Ghadhab, I. (2020). High-frequency trading and stock liquidity: An intraday analysis. Research in International Business and Finance, 101235.
• Baldauf, M., & Mollner, J. (2020). High-frequency trading and market performance. The Journal of Finance, 75(3), 1495-1526.
• Barbara, B., Sójka., & Krzysztof, E. (2020). What is the best proxy for liquidity in the presence of extreme illiquidity?. Emerging Markets Review, 43, 100695.
• Biais, B., Faoucalt, T., & Moninas, S. (2014). Equilibrium fast trading. Working Paper, 968/2013. Paris: HEC.
• Boehmer, E., Fong, K. Y., & Wu, J. J. (2015). International tvidence on algorithmic trading. In FMA, Annual Meeting Paper.
• Caspi, I. (2016). Testing for a housing bubble at the national and regional level: The case of Israel. Empir Econ, 51(2), 483-516.
• Celik, M. S., Ozturk, M. B., & Haykir, O. (2022). The effect of technological developments on the stock market: evidence from emerging market. Applied Economics Letters, 31(2), 118-121.
• Ekinci, C., & Ersan, O. (2022). High-frequency trading and market quality: The case of a “slightly exposed” market. International Review of Financial Analysis, 79.
• Ersan, O., & Ekinci, C. (2016). Algorithmic and high-frequency trading in Borsa Istanbul. Borsa Istanbul Review, 16(4), 233-248.
• Foucault, T., Hombert, J., & Rosu, I. (2013). News trading and speed. Working Paper. Paris: HEC.
• Garman, M.B., & Klass, M.J. (1980). On the estimation of security price volatilities from historical data. The Journal of Business, 53, 67-78.
• Glindro, E. T., & Delloro, V. K. (2010). Identifying and measuring asset price bubbles in the Philippines. BSP Working Paper.
• Glossner, S. Matos, P.P., Ramelli, S., & Wagner, A.F. (2020). Where do institutional investors seek shelter when disaster strikes? Evidence from Covid-19, CEPR Discussion Papers, 15070.
• Hardouvelis, G. (1988). Evidence on stock market speculative bubbles: Japan, the United States and Great Britain. Federal Reserve Bank of New York Quarterly Review, 4–16.
• Harras, G., & Sornette, D. (2011) How to grow a bubble: A model of myopic adapting agents, in press in J. Economic Behavior and Organization (in press).
• Hasbrouck, J., & Saar, G. (2013). Low-latency trading. Journal of Financial Markets, 16(4), 646-679.
• Hendershott, T., Jones, C., & Menkveld, A. (2011). Does algoritmic trading improve liquidity?. Journal of Finance, 66(1), 1-33.
• Hirshleifer, D. (2015). Behavioral Finance, Annual Review of Financial Economics, 7, 133-159.
• Jarnecic, E., & Snape, M. (2014). The provision of liquidity by high-frequency participants. Financial Review, 49(2), 371–394.
• Li, K., Cooper, R., & Van Vlıet, B. (2018). How does high-frequency trading affect low-frequency trading? Journal of Behavioral Finance, 19(2), 235-248.
• Menkveld, A. J. (2013). High-frequency trading and the new market makers. Journal of Financial Markets, 16(4), 712-740.
• Mitra, A., & Choudhuri, S. (2016) A study on rational price bubble in S&P Bse sensex. International Journal of Business Quantitative Economics and Applied Management Research, 3(4), 13-19.
• Patterson, S. (2012). Dark pools: The rise of the machine traders and the rigging of the U.S. stock market. Random House.
• Philips, M. (2013). How the robots lost: High-frequency trading’s rise and fall. Bloomberg Businessweek. Phillips, P. C. B., Wu, Y., & Yu, J. (2011). Explosive behavior in the 1990s Nasdaq: When did exuberance escalate asset values? International Economic Review, 52(1), 201-226.
• Phillips, P. C., Shi, S., & Yu, J. (2015). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International Economic Review, 56(4), 1043-1078.
• Rappoport, P., & White, E. (1991). Was there a bubble in the 1929 stock market? NBER Working Paper No. 3612.
• Richard, G. A., Jane, M. B., Björn, H., & Birger, N. (2015). Liquidity: Systematic liquidity, commonality, and high-frequency trading İçinde Greg N. Gregoriou, (Ed.). The Handbook of High-Frequency Trading, Academic Press, (pp.197-214).
• Singh, S., Vats, N., Jain, P., & Yadav, S. S. (2018). Rational bubbles in the Indian Stock Market: Empirical evidence from the NSE 500 Index. Finance India. XXXII (2), 457-478.
• Tran, T. B. N. (2017). Speculative bubbles in emerging stock markets and macroeconomic factors: A new empirical evidence for Asia and Latin America. Research in International Business and Finance, 42(C), 454-467.
• Ugurlu, E. (2023). Kesikli seçim modelleri (Logit, Multinomial Logit, Ordered Logit, Sıralı Lojit, Lojistik Model, Marjinal Etki, Odds Oranı). Retrieved from https://www.researchgate.net/ publication/281647356_Kesikli_Secim_Modelleri_Logit_Multinomial_Logit_Ordered_Logit_ Sirali_Lojit_Lojistik_Model_Marjinal_EtkiOdds_Orani. Accessed: Mar 21 2022.