scholarly journals High-Frequency Trading Competition

2018 ◽  
Vol 54 (4) ◽  
pp. 1469-1497 ◽  
Author(s):  
Jonathan Brogaard ◽  
Corey Garriott
Keyword(s):  
High Frequency ◽  
Stock Exchange ◽  
Market Liquidity ◽  
High Frequency Trading ◽  
Toronto Stock Exchange ◽  
Bid Ask Spreads

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.

scholarly journals Aggressive and Defensive High‑Frequency Trading and its Impact on Liquidity of German Stock Market

2016 ◽  
Vol 64 (6) ◽  
pp. 1911-1918
Author(s):  
Juraj Hruška
Keyword(s):  
High Frequency ◽  
Stock Exchange ◽  
Low Frequency ◽  
Positive Influence ◽  
Market Liquidity ◽  
High Frequency Trading ◽  
Market Manipulation ◽  
Limit Orders ◽  
Market Risks ◽  
German Stock

Algorithmic trading and especially high frequency trading is the concern of the current research studies as well as legislative authorities. It is also the subject of criticism mostly from low frequency traders and long-term institutional investors. This is due to several cases of market manipulation and flash crashes in the previous years. Advocates of this trading mechanism claim that it has large positive influence on the market, such as liquidity growth by lowering spreads and others. This paper is focused on testing the relationship between market liquidity of shares traded on Frankfurt Stock Exchange and HFT activity on European stock markets. Author proposes own methodology for measuring dynamics in HFT activity, without knowledge of original market messages. Liquidity is measured by various from of price spreads. Econometrical methods for panel regression are used to determine these relations. Results of this paper will reveal the relevance of the HFT trader’s main argument about creating liquidity and hence reducing market risks related with high spreads and low number of limit orders.

scholarly journals Determinants of price reversal in high-frequency trading: empirical evidence from Indonesia

2020 ◽  
Vol 17 (1) ◽  
pp. 175-187 ◽  
Author(s):  
Perdana Wahyu Santosa
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Stock Exchange ◽  
High Frequency Trading ◽  
High Price ◽  
Research Approach ◽  
Price Model ◽  
Bid Ask Spread ◽  
Price Reversal ◽  
New Research

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.

Hidden and Fast Liquidity - Hidden Orders and High-Frequency Trading

e-Finanse ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 27-35
Author(s):  
Martins Carlos Jorge Lenczewski
Keyword(s):  
High Frequency ◽  
Current Knowledge ◽  
Market Liquidity ◽  
High Frequency Trading ◽  
Specific Topic ◽  
Literature Study ◽  
Actual Knowledge ◽  
Price Effects ◽  
The Us ◽  
Hidden Orders

AbstractThis work focuses on two of the more frequent practices in financial (especially capital) markets -the use of hidden orders and High-Frequency Trading (HFT). Although the use of each of them may reach 40% of the market turnover - even 60% for HFT, the actual knowledge on how they affect liquidity, prices, and market structure is still limited - especially if they are combined. The presence of both of these practices may look controversial, as it seems to be going in the opposite direction to what some of the goals that market regulators try to reach - transparency and increase of market liquidity. Additionally, their use suggests first, to give a clear advantage to some traders while not knowing the exact consequences to others. The aim of this paper is, by performing a literature study, to structure the current knowledge on a very specific topic in the area of market microstructure - the use of hidden orders and High-Frequency Trading. This paper tries to show the motivations, strategies, and eventual price effects behind hidden orders and High-Frequency Trading. It is also important to mention that this paper is based on scarce empirical research available (mainly for the US market) and as such, it is intended to encourage further analysis and research on this important topic.

scholarly journals Impact of High-Frequency Trading on the Stock Returns of Large and Small Companies in the Tehran Stock Exchange

2016 ◽  
Vol 8 (4) ◽  
pp. 216
Author(s):  
Ahmad Sarlak ◽  
Zahra Talei
Keyword(s):  
Stock Returns ◽  
High Frequency ◽  
Stock Exchange ◽  
Financial Statements ◽  
Total Sample ◽  
Linear Functions ◽  
High Frequency Trading ◽  
Small Companies ◽  
Exchange Market ◽  
The Past

<p>The main objective of this study is to evaluate the effect of high-frequency trading on stock returns of the Exchange market in Tehran Stock Exchange. The research methodology in this study is in terms of the purpose, functional and in terms of the method of data collection, descriptive and in terms of the type, solidarity.</p><p>Statistical society of this research is all companies in the Tehran Stock Exchange which in the past two years had been active in the stock market. In this study, companies are divided into two categories: large and small companies. Large companies that their assets logarithm is greater than the average total sample and small companies that their assets logarithm is less than the average total sample. To collect information has been used from the financial statements of accepted companies in Tehran Stock Exchange.</p><p>MATLAB software has been used for data analysis.</p>Used tests in this study are include (DF) Dickey-Fuller test, (ADF) Generalized Dickey-Fuller test, Phillips-Perron test, and time series methods. The results of this study show that the dynamics of stock returns of the Tehran Stock Exchange are non-linear functions and high Frequency trading of the large companies affect the turnover of small companies. As a result, volume of the high-frequency trading and the returns of small and large companies are different from each other.

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