Revealing high-frequency trading provision of liquidity with visualization

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Abstract

Liquidity is crucial for successful financial markets. It ensures that all investors are able to buy and sell assets quickly at a fair price. High Frequency Traders (HFTs) utilize sophisticated algorithms operating with extreme speed and are frequently cited as liquidity providers. The objective of this paper is to investigate the liquidity provision of a number of HFTs to determine their effects on aggregate marketplace liquidity. We consider a large data set collected from the Australian Securities Exchange throughout 2013, providing a near complete picture of all trading activity. Our method is to consider temporal bar charts, association scatterplots, faceted plots and network diagrams to provide visualizations that yield both novel and conventional insights into how HFTs are operating in the market, specifically with respect to liquidity provision. Consistent with HFTs avoiding adverse selection, our results show that on aggregate, HFTs often consume rather than provide liquidity. Furthermore, liquidity consumption often occurs very quickly over intra-millisecond time periods. We conclude that HFTs are not exclusively focused on liquidity provision.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Software Engineering and Information Management (ICSIM 2019)
EditorsEko K. Budiardjo, Mohamed Sbihi
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages157-165
Number of pages9
Edition1st
ISBN (Print)9781450366427
DOIs
Publication statusPublished - 2019
EventInternational Conference on Software Engineering and Information Management 2019 - Bali, Indonesia
Duration: 10 Jan 201913 Jan 2019
Conference number: 2nd
http://www.icsim.org/icsim2019.html

Publication series

NameACM International Conference Proceeding Series

Conference

ConferenceInternational Conference on Software Engineering and Information Management 2019
Abbreviated titleICSIM 2019
CountryIndonesia
CityBali
Period10/01/1913/01/19
Internet address

Keywords

  • Data visualization
  • High frequency trading
  • Large data
  • Tick data

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