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Farklı Bilgi Düzeylerinde İnsan Yatırımcılara Karşı Algoritmik Ticaret

Cilt 10 Sayı 75 kapak

Abstract

Algorithmic Trading Versus Human Traders at Different Information Levels

In this study, it was investigated whether different levels of information create advantages or disadvantages in the stock market. For this purpose, net profit rates at the end of the period were calculated for two different investors. Investors are separated in terms of access to information differences. The fundamental investor is evaluated under the assumption of asymmetric information whereas the chartist investor is done under the assumption of perfect information. In the current study, the fundamental investor makes only one transaction, while the chartist investor acts according to the indicator The moving average convergence-divergence indicator (MACD) since it makes algorithmic transactions. The transactions were made based on trend periods. At the end of the trend, the net profit rates have been calculated to find out to see whether the information differences create commercial inequality.

In this context, two companies known as high in volume and worldwide known in Borsa Istanbul, where algorithms are also used, are discussed. Transactions for two different investors are started and completed on the same trend dates. When the transactions made in 20 different trend periods are compared, it is concluded that the chartist investor is more advantageous than the fundamental investor. As a result, a chartist investor who has perfect information is more advantageous for financial markets, as expected.

Keywords Algorithmic Trade, Investment, Asymmetric Information, Perfect Information, Stocks

Öz

Bu çalışmada, farklı bilgi düzeylerinin borsada avantaj yaratıp yaratmadığı araştırılmıştır. Bu amaçla iki farklı yatırımcı için dönem sonu net kâr oranları hesaplanmıştır. Yatırımcılar bilgiye erişim farklılıkları açısından ayrılmaktadır. Temel yatırımcı asimetrik bilgi varsayımı altında, teknik yatırımcı ise tam bilgi varsayımı altında değerlendirilmiştir. Çalışmada, temel yatırımcı sadece bir işlem yaparken, teknik yatırımcı algoritmik işlemler yaptığı için, MACD indikatörüne göre hareket etmektedir. İşlemler trend dönemlerine göre yapılmıştır. Trendin sonunda, bilgi farklılıklarının işlemlerde eşitsizlik yaratıp yaratmadığını görülmesi için net kâr oranları hesaplanmıştır.

Bu bağlamda algoritmaların da kullanıldığı Borsa İstanbul'da, hacmi yüksek ve dünya çapında bilinen iki şirket ele alınmaktadır. İki farklı yatırımcı için işlemler aynı trend tarihlerinde başlatılmış ve tamamlanmıştır. 20 farklı trend döneminde yapılan işlemler karşılaştırıldığında, teknik yatırımcının temel yatırımcıya göre daha avantajlı olduğu sonucuna varılmıştır. Sonuç olarak, tam bilgiye sahip olan teknik yatırımcı, beklendiği gibi finansal piyasalar için daha avantajlı konumdadır.

Anahtar Kelimeler Algoritmik Ticaret, Yatırım, Asimetrik Bilgi, Tam Bilgi, Hisse Senetleri

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Kaynakça

  1. Akerlof, G. A. (1970): The market for “lemons”: Quality uncertainty and the market mechanism. In Uncertainty in economics (pp. 235-251). Academic Press, https://doi.org/10.1016/B978-0-12-214850-7.50022-X DOI: https://doi.org/10.2307/1879431
  2. Appel, G. (2005): Technical analysis: power tools for active investors. FT Press.
  3. Benamraoui, A., Alwardat, Y. (2019): Asymmetric Information and Islamic Financial Contracts. International Journal of Economics and Finance, 11(1), 1-96, https://doi.org/10.5539/ijef.v11n1p96 DOI: https://doi.org/10.5539/ijef.v11n1p96
  4. Biais, B., Foucault, T., Moinas, S. (2011): Equilibrium high frequency trading. In Proceedings from the fifth annual Paul Woolley Centre conference, London School of Economics, https://doi.org/10.1016/j.jfineco.2015.03.004 DOI: https://doi.org/10.1016/j.jfineco.2015.03.004
  5. Brogaard, J., Hendershott, T., Riordan, R. (2014): High-frequency trading and price discovery. The Review of Financial Studies, 27(8), 2267-2306, https://doi.org/10.1093/rfs/hhu032 DOI: https://doi.org/10.1093/rfs/hhu032
  6. Carrion, A. (2013): Very fast money: High-frequency trading on the NASDAQ. Journal of Financial Markets, 16(4), 680-711, https://doi.org/10.1016/j.finmar.2013.06.005 DOI: https://doi.org/10.1016/j.finmar.2013.06.005
  7. Chaboud, A. P., Chiquoine, B., Hjalmarsson, E., Vega, C. (2014): Rise of the Machines: Algorithmic trading in the foreign exchange market. The Journal of Finance, 69(5), 2045-2084, https://doi.org/10.1111/jofi.12186 DOI: https://doi.org/10.1111/jofi.12186
  8. Foucault, T., Hombert, J., Rosu, I. (2013): News trading and speed. HEC Paris Research Paper No. 975/2013. DOI: https://doi.org/10.2139/ssrn.2188822
  9. Freund, W. C., Larrain, M., Pagano, M. S. (1997): Market efficiency before and after the introduction of electronic trading at the Toronto stock exchange. Review of Financial Economics, 6(1), 29-56, https://doi.org/10.1016/S1058-3300(97)90013-6 DOI: https://doi.org/10.1016/S1058-3300(97)90013-6
  10. Frino, A., Mollica, V., Monaco, E., Palumbo, R. (2017): The effect of algorithmic trading on market liquidity: Evidence around earnings announcements on Borsa Italiana. Pacific-Basin Finance Journal, 45, 82-90, https://doi.org/10.1016/j.pacfin.2016.07.003 DOI: https://doi.org/10.1016/j.pacfin.2016.07.003
  11. Garcia, D., Schweitzer, F. (2015): Social signals and algorithmic trading of Bitcoin. Royal Society open science, 2(9), 150288, https://doi.org/10.1098/rsos.150288 DOI: https://doi.org/10.1098/rsos.150288
  12. Groß-Klußmann, A., Hautsch, N. (2011): When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions. Journal of Empirical Finance, 18(2), 321-340, https://doi.org/10.1016/j.jempfin.2010.11.009 DOI: https://doi.org/10.1016/j.jempfin.2010.11.009
  13. Hagströmer, B., Nordén, L. (2013): The diversity of high-frequency traders. Journal of Financial Markets, 16(4), 741-770, https://doi.org/10.1016/j.finmar.2013.05.009 DOI: https://doi.org/10.1016/j.finmar.2013.05.009
  14. Hendershott, T., Riordan, R. (2011): High frequency trading and price discovery. Manuscript, Univesity of California, Berkeley, 3, https://dx.doi.org/10.2139/ssrn.1928510 DOI: https://doi.org/10.2139/ssrn.1928510
  15. Hirschey, N. (2018): Do high-frequency traders anticipate buying and selling pressure? Available at SSRN 2238516, https://doi.org/10.1287/mnsc.2020.3608 DOI: https://doi.org/10.1287/mnsc.2020.3608
  16. Hoffmann, P. (2014): A dynamic limit order market with fast and slow traders. Journal of Financial Economics, 113(1), 156-169, https://doi.org/10.1016/j.jfineco.2014.04.002 DOI: https://doi.org/10.1016/j.jfineco.2014.04.002
  17. Ito, T., Yamada, K., Takayasu, M., Takayasu, H. (2012): Free lunch! Arbitrage opportunities in theforeign exchange markets (No. w18541). National Bureau of Economic Research, https://doi.org/10.3386/w18541 DOI: https://doi.org/10.3386/w18541
  18. Ito, T., Yamada, M. (2015): High-frequency, algorithmic spillovers between nasdaq and forex (No.w21122). National Bureau of Economic Research, https://doi.org/10.3386/w21122 DOI: https://doi.org/10.3386/w21122
  19. Kondor, P. (2009): Risk in dynamic arbitrage: Price effects of convergence trading, Journal of Finance 664, 631–655, https://doi.org/10.1111/j.1540-6261.2009.01445.x DOI: https://doi.org/10.1111/j.1540-6261.2009.01445.x
  20. Kozhan, R., Tham, W. (2009): Arbitrage Opportunity: A Blessing or a Curse. Warwick Business, School, Working Paper. DOI: https://doi.org/10.2139/ssrn.1325252
  21. Kulkarnı, A. D., More, A. (2014): An Application of Moving Average Convergence and Divergence (MACD) Indicator on Selected Stocks Listed on Bombay Stock Exchange (BSE). Oriental Journal of Computer Science& Technology, 7 (3), 396-400.
  22. Martinez, V. H., Rosu, I. (2013): High frequency traders, news and volatility. In AFA 2013 San Diego Meetings Paper. DOI: https://doi.org/10.2139/ssrn.2024391
  23. Oehmke, M. (2009): Gradual arbitrage, Working paper, Columbia University. DOI: https://doi.org/10.2139/ssrn.1364126
  24. Paddrik, M., Hayes, R., Todd, A., Yang, S., Beling, P., Scherer, W. (2012): An agent-based model of the E-Mini S&P 500 applied to Flash Crash analysis. In 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (pp. 1-8). IEEE. DOI: https://doi.org/10.1109/CIFEr.2012.6327800
  25. Papachristou, G., Papadamou, S., Spyromitros, E. (2018): Asymmetric price responses to stock addition to and deletion from the Athens Stock Exchange Index. Managerial Finance, https://doi.org/10.1108/MF-12-2016-0350 DOI: https://doi.org/10.1108/MF-12-2016-0350
  26. Ramos, H. P., Perlin, M. S. (2020): Does algorithmic trading harm liquidity? Evidence from Brazil. The North American Journal of Economics and Finance, 101243, https://doi.org/10.1016/j.najef.2020.101243 DOI: https://doi.org/10.1016/j.najef.2020.101243
  27. Ravi, R., Hong, Y. (2015): Information asymmetry around S&P 500 index changes. Review of Accounting and Finance, 14(2), 106-127, https://doi.org/10.1108/RAF-04-2014-0046 DOI: https://doi.org/10.1108/RAF-04-2014-0046
  28. Riordan, R., Storkenmaier, A., Wagener, M., Zhang, S. S. (2013): Public information arrival: Price discovery and liquidity in electronic limit order markets. Journal of Banking & Finance, 37(4), 1148-1159, https://doi.org/10.1016/j.jbankfin.2012.11.008 DOI: https://doi.org/10.1016/j.jbankfin.2012.11.008
  29. Seo, J. Y., Chai, S. (2013): The role of algorithmic trading systems on stock market efficiency. Information Systems Frontiers, 15(5), 873-888, https://doi.org/10.1007/s10796-013-9442-9 DOI: https://doi.org/10.1007/s10796-013-9442-9
  30. Syamala, S. R., Wadhwa, K. (2020): Trading performance and market efficiency: Evidence from algorithmic trading. Research in International Business and Finance, 54, 101283, https://doi.org/10.1016/j.ribaf.2020.101283 DOI: https://doi.org/10.1016/j.ribaf.2020.101283
  31. Tse, Y., Zabotina, T. V. (2001): Transaction costs and market quality: Open outcry versus electronic trading. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 21(8), 713-735, https://doi.org/10.1002/fut.1802 DOI: https://doi.org/10.1002/fut.1802
  32. Zhang, S. Sarah. (2012): Need for Speed: An Empirical Analysis of Hard and Soft Information in a High Frequency World." SSRN Electronic Journal, https://dx.doi.org/10.2139/ssrn.1985951 DOI: https://doi.org/10.2139/ssrn.1985951