Trade Signing in Fast Markets

Carrion, Allen; Kolay, Madhuparna

doi:10.2139/ssrn.2489868

Cited by 5 publications

(7 citation statements)

References 32 publications

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“…According to previous research, the classification success rate of the tick rule ranges from 72.2% (Theissen, 2001) to 92.15% (Aktas & Kryzanowski, 2014) on the U.S. and non-U.S. stock markets. Of the research cited in this work, the study by Carrion and Kolay (2020) presents the similar fast trading environment by using high-frequency NASDAQ data stamped to seconds. And the accuracy of the tick rule assessed in this study is close to the corresponding values of individual stocks in Carrion and Kolay (2020), namely from 69.75% to 83.34%.…”

Section: Discussionmentioning

confidence: 99%

“…Tests by Chakrabarty et al (2007) on NASDAQ stocks traded on INET and ArcaEx revealed that the overall success rate of the tick rule was 75.4% during the sample period. A recent examination by Carrion and Kolay (2020) showed that the classification success rate of the tick rule was 78.62% in a sample of data stamped to seconds from NASDAQ HFT database over a subset of dates during the period of 2008 to 2010 when trades were more frequent than before. And the classification success rate ranged from 69.75% to 83.34% across individual stocks.…”

Section: Literature Reviewmentioning

confidence: 99%

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The Accuracy of the Tick Rule in the Bitcoin Market

Zhai

2021

Sage Open

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The tick rule is one of the most popular trade classification algorithms used when an order initiator in market data is not signed. Using 11.9 million trades of Bitcoin/USD on Bitstamp, this article tests the accuracy of the tick rule in the Bitcoin market. Evidence indicates that the overall success rate of the tick rule is 76.87%. It is also shown that the tick rule is inclined to fail in discerning trade intentions when there is a long period of time between trades. Furthermore, order imbalances computed using the tick rule lack sufficient accuracy in the Bitcoin market.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

The Accuracy of the Tick Rule in the Bitcoin Market

Zhai

2021

Sage Open

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“…This is typically a problem in many data sets because trades and quotes observed at coarse time intervals are either not individually sequenced or are not sequenced against each other (i.e., trades vs. quotes) within each time interval. Our procedure still suffers from the limitations in the Lee and Ready (1991) trade-signing algorithm, but most studies suggest that this approach works very well provided that quotes and trades are correctly matched (e.g., Carrion and Kolay (2014)).…”

Section: Discussionmentioning

confidence: 99%

Interactions among High-Frequency Traders

Benos¹,

Brugler²,

Hjalmarsson³

et al. 2017

J. Financ. Quant. Anal.

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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. Keywords: High-Frequency Trading; Correlated Trading Strategies; Price Discovery. JEL Classification: G10, G12, G14. * Benos is with the Bank of England, Threadneedle Street, London, EC2R 8AH, U.K.; Brugler is with the University of Melbourne, Department of Finance, Level 11, 198 Berkeley St, Victoria 3010, Australia; Hjalmarsson is with the University of Gothenburg, Department of Economics, Centre for Finance, Vasagatan 1, SE 405 30 Gothenburg, Sweden; and Zikes is with the Division of Financial Stability, Federal Reserve Board, 1801 K Street NW, Washington, DC 20037. Please address comments to the authors via e-mail at Evangelos. Benos@bankofengland.co.uk, james.brugler@unimelb.edu.au, erik.hjalmarsson@economics.gu.se, and Filip.Zikes@frb.gov. We are grateful to Satchit Sagade for his help in cleaning and processing the data. Helpful comments were provided by Monica Billio, Björn Hagströmer, Edwin Schooling Latter, Nick Vause, Graham Young, and seminar participants at the Bank of England, Bank of Greece, Copenhagen Business School, the Federal Reserve Board, the U.K. Financial Conduct Authority, University of Piraeus, University of York, the conference on The Development of Securities Markets: Trends, Risks and Policies at Bocconi University, the 2015 conference of the International Association of Applied Econometrics, and the 2014 Ioannina Meeting on Applied Economics and Finance. The views in this paper are solely the responsibility of the authors and should not be interpreted as representing either the views of the Bank of England or any of its committees, or the U.K. I IntroductionHigh-frequency trading, where automated computer traders interact at lightningfast speed with electronic trading platforms, has become an important feature of many modern markets. The rapid growth and increased prominence of these ultra fast traders have given rise to concerns regarding their impact on market quality and stability. Recent events, such as the "flash crashes" in U.S. equity markets on May 6, 2010 and U.S. Treasury markets on October 15, 2014, have highlighted such worries. Over the past few years, numerous empirical studies have analyzed the market impact of high-frequency trading (HFT), as well as algorithmic trading (AT) more generally.1,2 With some recent exceptions, most of these studies have analyzed aggregate measures of HFT and AT in various markets. 3 The curre...

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“…In this paper, trades are classified as buys or sells using an algorithm like the one in Lee and Ready (1991). Carrion and Kolay (2020) show that this algorithm maintains accuracy in fast markets. Following one of the methods proposed in Chordia and Subrahmanyam (2004), I define order imbalances as the excess of buyer‐initiated trades over seller‐initiated trades, divided by the total traded volume, as follows:

\begin{equation}{\rm{O}}{{\rm{I}}_x} = \frac{{{\rm{Buy}}\,{\rm{Volum}}{{\rm{e}}_x} - {\rm{Sell}}\,{\rm{Volum}}{{\rm{e}}_x}}}{{{\rm{Buy}}\,{\rm{Volum}}{{\rm{e}}_x} + {\rm{Sell}}\,{\rm{Volum}}{{\rm{e}}_x}}}.\end{equation}

…”

Section: Model and Methodologymentioning

confidence: 99%

ETFs’ two‐sided trading costs and order imbalances

Lachance

2022

The Financial Review

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This paper proposes an integrated trading cost measure for exchange-traded funds (ETFs) that includes premiums and spreads. The measure is computed separately for buyers and sellers as the distance between the ETF's bid/ask prices and a new measure of intraday intrinsic value constructed with the ETF's daily holdings. The two-sided measure shows that order imbalances increase trading costs in the direction of the imbalance, bias midpoint quotes, and result in observed premiums and discounts. It also provides a link between these higher trading costs and the need for authorized participants to pass through the costs associated with primary market activity.

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Trade Signing in Fast Markets

Cited by 5 publications

References 32 publications

The Accuracy of the Tick Rule in the Bitcoin Market

The Accuracy of the Tick Rule in the Bitcoin Market

Interactions among High-Frequency Traders

ETFs’ two‐sided trading costs and order imbalances

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