Journal articles: 'High frequency trading'

1

Rebonato, Riccardo. "High-frequency Trading." Quantitative Finance 15, no. 8 (July 9, 2015): 1267–71. http://dx.doi.org/10.1080/14697688.2015.1050869.

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Chordia, Tarun, Amit Goyal, Bruce N. Lehmann, and Gideon Saar. "High-frequency trading." Journal of Financial Markets 16, no. 4 (November 2013): 637–45. http://dx.doi.org/10.1016/j.finmar.2013.06.004.

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Lattemann, Christoph, Peter Loos, Johannes Gomolka, Hans-Peter Burghof, Arne Breuer, Peter Gomber, Michael Krogmann, et al. "High Frequency Trading." WIRTSCHAFTSINFORMATIK 54, no. 2 (March 2, 2012): 91–101. http://dx.doi.org/10.1007/s11576-012-0311-9.

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Gomber, Peter, and Martin Haferkorn. "High-Frequency-Trading." WIRTSCHAFTSINFORMATIK 55, no. 2 (February 20, 2013): 99–102. http://dx.doi.org/10.1007/s11576-013-0355-5.

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Lattemann, Christoph, Peter Loos, Johannes Gomolka, Hans-Peter Burghof, Arne Breuer, Peter Gomber, Michael Krogmann, et al. "High Frequency Trading." Business & Information Systems Engineering 4, no. 2 (March 6, 2012): 93–108. http://dx.doi.org/10.1007/s12599-012-0205-9.

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Gomber, Peter, and Martin Haferkorn. "High-Frequency-Trading." Business & Information Systems Engineering 5, no. 2 (February 26, 2013): 97–99. http://dx.doi.org/10.1007/s12599-013-0255-7.

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Patil, Mr Mihir Rajan. "Algorithmic Trading & High Frequency Trading." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 1640–42. http://dx.doi.org/10.22214/ijraset.2019.6275.

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Li, Kun, Rick Cooper, and Ben Van Vliet. "How Does High-Frequency Trading Affect Low-Frequency Trading?" Journal of Behavioral Finance 19, no. 2 (November 7, 2017): 235–48. http://dx.doi.org/10.1080/15427560.2017.1376669.

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Brogaard, Jonathan, and Corey Garriott. "High-Frequency Trading Competition." Journal of Financial and Quantitative Analysis 54, no. 4 (September 19, 2018): 1469–97. http://dx.doi.org/10.1017/s0022109018001175.

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Theory on high-frequency traders (HFTs) predicts that market liquidity for a security decreases in the number of HFTs trading the security. We test this prediction by studying a new Canadian stock exchange, Alpha, that experienced the entry of 11 HFTs over 4 years. We find that bid–ask spreads on Alpha converge to those at the Toronto Stock Exchange as more HFTs trade on Alpha. Effective and realized spreads for non-HFTs improve as HFTs enter the market. To explain the contrast with theory, which models the HFT as a price competitor, we provide evidence more consistent with HFTs fitting a quantity-competitor framework.

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Quelhas, José Manuel. "High-frequency trading (HFT)." Boletim de Ciências Económicas 58 (2015): 369–400. http://dx.doi.org/10.14195/0870-4260_58_8.

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Chen, Marie, and Corey Garriott. "High-frequency trading and institutional trading costs." Journal of Empirical Finance 56 (March 2020): 74–93. http://dx.doi.org/10.1016/j.jempfin.2019.12.002.

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Rosenbaum, Mathieu. "Algorithmic and High-Frequency Trading." Quantitative Finance 18, no. 1 (October 27, 2017): 7–8. http://dx.doi.org/10.1080/14697688.2017.1380983.

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13

김영주. "Regulation of High-Frequency Trading." Korean Journal of Securities Law 15, no. 2 (August 2014): 129–84. http://dx.doi.org/10.17785/kjsl.2014.15.2.129.

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14

Goldstein, Michael A., Pavitra Kumar, and Frank C. Graves. "Computerized and High-Frequency Trading." Financial Review 49, no. 2 (April 7, 2014): 177–202. http://dx.doi.org/10.1111/fire.12031.

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15

Lopez de Prado, Marcos. "Algorithmic and High Frequency Trading." Quantitative Finance 16, no. 8 (March 14, 2016): 1175–76. http://dx.doi.org/10.1080/14697688.2016.1143619.

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Felker, Travis, Vadim Mazalov, and Stephen M. Watt. "Distance-based High-frequency Trading." Procedia Computer Science 29 (2014): 2055–64. http://dx.doi.org/10.1016/j.procs.2014.05.189.

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17

Wahyu Santosa, Perdana. "Determinants of price reversal in high-frequency trading: empirical evidence from Indonesia." Investment Management and Financial Innovations 17, no. 1 (March 19, 2020): 175–87. http://dx.doi.org/10.21511/imfi.17(1).2020.16.

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This article analyzes whether the factors of the mechanism of high-frequency trading (HFT) or intraday trading affect the process of price reversal and continuation. The price reversal phenomenon is gaining importance rapidly due to the increasingly intensive use of IT/Fintech-based trading automation facilities on the Indonesia Stock Exchange. However, one knows little about how their trading affects volatility and liquidity pressures that cause price reversals. A new research approach uses the factors of market microstructure mechanism based on high-frequency data (HFD-intraday). The research method uses purposive random sampling, which classified price fractions into three groups, specifically low price, medium price, and high price, which are analyzed by logistic panel regression. The research variables used include price reversal (dependent), stock return, trading volume, transaction frequency, volume/frequency (V/F) proxy, volatility, and liquidity. According to low price model research findings, all variables show a significant effect on price reversal; for medium price model, all variables except liquidity show a significant effect on price reversal; and for high price model, all variables have a significant effect on price reversal, except trading volume and volatility. In conclusion, low price shares tend to have higher price reversal probability compared to continuity because they tend to be liquid, low institutional ownership, and minimal reporting/analysis and are controlled by HFTs (uninformed traders). Some variables are not significant because of the bounce effect around the bid-ask spread. AcknowledgmentMany thanks to Armida S. Alisjahbana, Roy H. Sembel, Budiono, Rahardi S. Rahmanto, and the anonymous referee/reviewer for valuable inputs and feedback.

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18

Weber, Bruce. "HIGH FREQUENCY TRADING: THE GROWING THREAT OF ROGUE TRADING." Business Strategy Review 22, no. 2 (June 2011): 50–53. http://dx.doi.org/10.1111/j.1467-8616.2011.00751.x.

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19

Podobnik, Boris, Duan Wang, and H. Eugene Stanley. "High-frequency trading model for a complex trading hierarchy." Quantitative Finance 12, no. 4 (April 2012): 559–66. http://dx.doi.org/10.1080/14697688.2012.664928.

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20

Morelli, Michael. "Implementing High Frequency Trading Regulation: A Critical Analysis of Current Reforms." Michigan Business & Entrepreneurial Law Review, no. 6.2 (2017): 201. http://dx.doi.org/10.36639/mbelr.6.2.implementing.

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Technological developments in securities markets, most notably high frequency trading, have fundamentally changed the structure and nature of trading over the past fifty years. Policymakers, both domestically and abroad, now face many new challenges influencing the secondary market’s effectiveness as a generator of economic growth and stability. Faced with these rapid structural changes, many are quick to denounce high frequency trading as opportunistic and parasitic. This article, however, instead argues that while high frequency trading presents certain general risks to secondary market efficiency, liquidity, stability, and integrity, the practice encompasses a wide variety of strategies, many of which can enhance, not inhibit, the secondary trading market’s core goals. This article proposes a regulatory model aimed at maximizing high frequency trading’s beneficial effects on secondary market functions. The model’s foundation, however, requires information. By analyzing more data on how high frequency traders interact with markets, regulators can assess the viability and scope of other potentially worthwhile measures targeting more general market threats. Likewise, regulators can determine who is in the best position to bear supervisory responsibility for particular trading activities: agencies, exchanges, traders, or some combination thereof. Crucially, the model also calls on regulators to share information on a global scale: trading no longer only affects a single exchange, a single asset class, or even a single country. By sharing information, regulators can enact more informed regulations, stabilize secondary markets, and minimize regulatory arbitrage. In short, high frequency trading can be a force for good, but a principled and coordinated effort is needed to ensure it fulfills that potential.

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21

Lenczewski Martins, Carlos Jorge. "Predatory Strategies in High-Frequency Trading." Annales Universitatis Mariae Curie-Skłodowska, sectio H, Oeconomia 51, no. 4 (January 9, 2018): 207. http://dx.doi.org/10.17951/h.2017.51.4.207.

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22

Cooper, Rick, and Ben Van Vliet. "Expected Return in High-Frequency Trading." Journal of Trading 10, no. 2 (March 31, 2015): 34–40. http://dx.doi.org/10.3905/jot.2015.10.2.034.

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23

Anil, Aditya, Ashwin Sudha Arun, Lalitha Ramchandar, and A. Balasundaram. "Parallelizing High-Frequency Trading using GPGPU." National Academy Science Letters 44, no. 5 (July 5, 2021): 465–70. http://dx.doi.org/10.1007/s40009-021-01064-9.

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24

Dannhauser, Bob. "High-Frequency Trading: Beyond the Hype." CFA Institute Magazine 26, no. 4 (July 2015): 46. http://dx.doi.org/10.2469/cfm.v26.n4.14.

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25

Musciotto, Federico, Jyrki Piilo, and Rosario N. Mantegna. "High-frequency trading and networked markets." Proceedings of the National Academy of Sciences 118, no. 26 (June 25, 2021): e2015573118. http://dx.doi.org/10.1073/pnas.2015573118.

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Financial markets have undergone a deep reorganization during the last 20 y. A mixture of technological innovation and regulatory constraints has promoted the diffusion of market fragmentation and high-frequency trading. The new stock market has changed the traditional ecology of market participants and market professionals, and financial markets have evolved into complex sociotechnical institutions characterized by a great heterogeneity in the time scales of market members’ interactions that cover more than eight orders of magnitude. We analyze three different datasets for two highly studied market venues recorded in 2004 to 2006, 2010 to 2011, and 2018. Using methods of complex network theory, we show that transactions between specific couples of market members are systematically and persistently overexpressed or underexpressed. Contemporary stock markets are therefore networked markets where liquidity provision of market members has statistically detectable preferences or avoidances with respect to some market members over time with a degree of persistence that can cover several months. We show a sizable increase in both the number and persistence of networked relationships between market members in most recent years and how technological and regulatory innovations affect the networked nature of the markets. Our study also shows that the portfolio of strategic trading decisions of high-frequency traders has evolved over the years, adding to the liquidity provision other market activities that consume market liquidity.

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26

Joshi, Harshit. "SML ON HIGH FREQUENCY TRADING DATA." International Journal of Engineering Applied Sciences and Technology 04, no. 11 (April 30, 2020): 123–27. http://dx.doi.org/10.33564/ijeast.2020.v04i11.023.

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27

Loveless, Jacob, Sasha Stoikov, and Rolf Waeber. "Online Algorithms in High-frequency Trading." Queue 11, no. 8 (August 2013): 30–41. http://dx.doi.org/10.1145/2523426.2534976.

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28

Jarrow, Robert, and Philip Protter. "Liquidity Suppliers and High Frequency Trading." SIAM Journal on Financial Mathematics 6, no. 1 (January 2015): 189–200. http://dx.doi.org/10.1137/140967702.

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29

BALDAUF, MARKUS, and JOSHUA MOLLNER. "High‐Frequency Trading and Market Performance." Journal of Finance 75, no. 3 (February 8, 2020): 1495–526. http://dx.doi.org/10.1111/jofi.12882.

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30

Van Vliet, Ben. "Capability satisficing in high frequency trading." Research in International Business and Finance 42 (December 2017): 509–21. http://dx.doi.org/10.1016/j.ribaf.2017.03.002.

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31

Chu, Jeffrey, Stephen Chan, and Yuanyuan Zhang. "High frequency momentum trading with cryptocurrencies." Research in International Business and Finance 52 (April 2020): 101176. http://dx.doi.org/10.1016/j.ribaf.2019.101176.

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32

Brogaard, Jonathan, Terrence Hendershott, and Ryan Riordan. "High-Frequency Trading and Price Discovery." Review of Financial Studies 27, no. 8 (May 14, 2014): 2267–306. http://dx.doi.org/10.1093/rfs/hhu032.

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33

Fernandez-Tapia, Joaquin. "High-Frequency Trading Meets Online Learning." Market Microstructure and Liquidity 02, no. 01 (June 2016): 1650003. http://dx.doi.org/10.1142/s2382626616500039.

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We propose an optimization framework for market-making in a limit order book, based on the theory of stochastic approximation. The idea is to take advantage of the iterative nature of the process of updating bid and ask quotes in order to make the algorithm optimize its strategy on a trial-and-error basis (i.e., online learning) using a variation of the stochastic gradient-descent algorithm. An advantage of this approach is that the exploration of the system by the algorithm is performed in run-time, so explicit specifications of price dynamics are not necessary, as is the case in the stochastic-control approach [(Gueant et al., 2013, Dealing with the Inventory Risk: A Solution to the Market Making Problem, Mathematics and Financial Economics 7(4), 477–507)]. For price/liquidity modeling, we consider a discrete-time variant of the Avellaneda–Stoikov model [(Avellaneda, M. and S. Stoikov, 2008, Liquidation in Limit Order Books with Controlled Intensity, Mathematical Finance 24(4), 627–650)] similar to its developent in the paper of Laruelle et al. [(Laruelle et al., 2013, Optimal Posting Price of Limit Orders: Learning by trading, Mathematics and Financial Economics 7(3), 359–403)] in the context of optimal liquidation tactics. Our aim is to set the ground for more advanced reinforcement learning techniques and to argue that the rationale of our method is generic enough to be extended to other classes of trading problems besides market-making.

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34

Loveless, Jacob, Sasha Stoikov, and Rolf Waeber. "Online algorithms in high-frequency trading." Communications of the ACM 56, no. 10 (October 2013): 50–56. http://dx.doi.org/10.1145/2507771.2507780.

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35

Virgilio, Gianluca Piero Maria. "High-frequency trading: a literature review." Financial Markets and Portfolio Management 33, no. 2 (June 2019): 183–208. http://dx.doi.org/10.1007/s11408-019-00331-6.

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36

Morelli, Michael. "Regulating Secondary Markets in the High Frequency Age: A Principled and Coordinated Approach." Michigan Business & Entrepreneurial Law Review, no. 6.1 (2016): 79. http://dx.doi.org/10.36639/mbelr.6.1.regulating.

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Abstract:

Technological developments in securities markets, most notably high frequency trading, have fundamentally changed the structure and nature of trading over the past 50 years. Policymakers both domestically and abroad now face many new challenges impacting the secondary market’s effectiveness as a generator of economic growth and stability. Faced with these rapid structural changes, many are quick to denounce high frequency trading as opportunistic and parasitic. This article, however, instead argues that while high frequency trading presents certain general risks to secondary market efficiency, liquidity, stability, and integrity, the practice encompasses a wide variety of strategies, many of which can enhance, not inhibit, the secondary trading market’s core goals. This article proposes a regulatory model aimed at maximizing high frequency trading’s beneficial effects on secondary market functions. The model’s foundation, however, requires information. By analyzing more data on how high frequency traders interact with markets, regulators can assess the viability and scope of other potentially worthwhile measures targeting more general market threats. Likewise, regulators can determine who is in the best position to bear supervisory responsibility for particular trading activities: agencies, exchanges, traders, or some combination thereof. Crucially, the model also calls on regulators to share information on a global scale: trading no longer only affects a single exchange, a single asset class, or even a single country. By sharing information, global regulations become more informed, secondary market stability is enhanced, and regulatory arbitrage is minimized. In short, high frequency trading can be a force for good, but a principled and coordinated effort is required to ensure it fulfills that potential.

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37

Benos, Evangelos, James Brugler, Erik Hjalmarsson, and Filip Zikes. "Interactions among High-Frequency Traders." Journal of Financial and Quantitative Analysis 52, no. 4 (July 25, 2017): 1375–402. http://dx.doi.org/10.1017/s0022109017000485.

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Using unique transactions data for individual high-frequency trading (HFT) firms in the U.K. equity market, we examine the extent to which the trading activity of individual HFT firms is correlated with each other and the impact on price efficiency. We find that HFT order flow, net positions, and total volume exhibit significantly higher commonality than those of a comparison group of investment banks. However, intraday HFT order flow commonality is associated with a permanent price impact, suggesting that commonality in HFT activity is information based and so does not generally contribute to undue price pressure and price dislocations.

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38

Baron, Matthew, Jonathan Brogaard, Björn Hagströmer, and Andrei Kirilenko. "Risk and Return in High-Frequency Trading." Journal of Financial and Quantitative Analysis 54, no. 3 (September 19, 2018): 993–1024. http://dx.doi.org/10.1017/s0022109018001096.

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We study performance and competition among firms engaging in high-frequency trading (HFT). We construct measures of latency and find that differences in relative latency account for large differences in HFT firms’ trading performance. HFT firms that improve their latency rank due to colocation upgrades see improved trading performance. The stronger performance associated with speed comes through both the short-lived information channel and the risk management channel, and speed is useful for various strategies, including market making and cross-market arbitrage. We find empirical support for many predictions regarding relative latency competition.

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39

Cooper, Ricky, Michael Davis, and Ben Van Vliet. "The Mysterious Ethics of High-Frequency Trading." Business Ethics Quarterly 26, no. 1 (January 2016): 1–22. http://dx.doi.org/10.1017/beq.2015.41.

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ABSTRACT:The ethics of high frequency trading are obscure, due in part to the complexity of the practice. This article contributes to the existing literature of ethics in financial markets by examining a recent trend in regulation in high frequency trading, the prohibition of deception. We argue that in the financial markets almost any regulation, other than the most basic, tends to create a moral hazard and increase information asymmetry. Since the market’s job is, at least in part, price discovery, we argue that simplicity of regulation and restraint in regulation are virtues to a greater extent than in other areas of finance. This article proposes criteria for determining which high-frequency trading strategies should be regulated.

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40

Liu, Chang, Huilin Shan, Zhihao Tian, and Hangyu Cheng. "Research on a quantitative trading strategy based on high-frequency trading." BCP Business & Management 26 (September 19, 2022): 320–28. http://dx.doi.org/10.54691/bcpbm.v26i.1942.

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This article aims to develop a quantitative trading strategy that maximizes profits while finding the best balance of risk and return. We built a high-frequency trading strategy model to maximize profits. We first used the Apriori algorithm to find frequency item sets in historical data before fitting the best daily dynamic position adjustment functions for gold and bitcoin using mathematical statistics and other methods based on price movements. Then we can trade to increase and decrease positions in gold and bitcoin based on the positions suggested by the dynamic position adjustment function. We also simulated three investors with different risk preferences trading using this high-frequency trading model for up to five years and obtained return of 266.05 %, 152.51 %, and 33.29 %, respectively.

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41

Jain, Archana, Chinmay Jain, and Christine X. Jiang. "Active Trading in ETFs: The Role of High-Frequency Algorithmic Trading." Financial Analysts Journal 77, no. 2 (March 4, 2021): 66–82. http://dx.doi.org/10.1080/0015198x.2020.1865694.

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42

Kearns, Michael, Alex Kulesza, and Yuriy Nevmyvaka. "Empirical Limitations on High-Frequency Trading Profitability." Journal of Trading 5, no. 4 (September 30, 2010): 50–62. http://dx.doi.org/10.3905/jot.2010.5.4.050.

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43

Abergel, Frédéric, Charles-Albert Lehalle, and Mathieu Rosenbaum. "Understanding the Stakes of High-Frequency Trading." Journal of Trading 9, no. 4 (September 30, 2014): 49–73. http://dx.doi.org/10.3905/jot.2014.9.4.049.

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44

Kohda, Shigeki, and Kenichi Yoshida. "Characteristics and Forecast of High-frequency Trading." Transactions of the Japanese Society for Artificial Intelligence 37, no. 5 (September 1, 2022): B—M44_1–9. http://dx.doi.org/10.1527/tjsai.37-5_b-m44.

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45

Chordia, Tarun, Amit Goyal, Bruce N. Lehmann, and Gideon Saar. "High-Frequency Trading." SSRN Electronic Journal, 2013. http://dx.doi.org/10.2139/ssrn.2278347.

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46

Gomber, Peter, Björn Arndt, Marco Lutat, and Tim Elko Uhle. "High-Frequency Trading." SSRN Electronic Journal, 2011. http://dx.doi.org/10.2139/ssrn.1858626.

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47

Puorro, Alfonso. "High Frequency Trading: Una Panoramica (High Frequency Trading: An Overview)." SSRN Electronic Journal, 2013. http://dx.doi.org/10.2139/ssrn.2369311.

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48

Draus, Sarah. "High Frequency Trading and Fundamental Trading." SSRN Electronic Journal, 2017. http://dx.doi.org/10.2139/ssrn.2980875.

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49

Biais, Bruno, Thierry Foucault, and Sophie Moinas. "Equilibrium High-Frequency Trading." SSRN Electronic Journal, 2012. http://dx.doi.org/10.2139/ssrn.2024360.

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50

Brogaard, Jonathan, Corey Garriott, and Anna Pomeranets. "High-Frequency Trading Competition." SSRN Electronic Journal, 2014. http://dx.doi.org/10.2139/ssrn.2435999.

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Journal articles on the topic 'High frequency trading'

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