IDEAS home Printed from https://ideas.repec.org/a/wly/jfutmk/v40y2020i5p749-760.html
   My bibliography  Save this article

Impact of algorithmic trading on speed of adjustment to new information: Evidence from interest rate derivatives

Author

Listed:
  • Alex Frino
  • Michael Garcia
  • Zeyang Zhou

Abstract

In February 2012, the Australian Securities Exchange introduced co‐location services for futures traders, thus providing a natural experiment to test the impact of algorithmic trading (AT) on the speed of adjustment and price discovery during scheduled macroeconomic releases. Our results demonstrate that, in the presence of AT, the speed of adjustment to new information has improved for both exchange‐traded futures and over‐the‐counter‐traded swaps. In addition, we find that the price discovery contribution of the futures market improves in the post‐AT period, with this improvement significant for macroeconomic announcement days.

Suggested Citation

  • Alex Frino & Michael Garcia & Zeyang Zhou, 2020. "Impact of algorithmic trading on speed of adjustment to new information: Evidence from interest rate derivatives," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 749-760, May.
    • Handle: RePEc:wly:jfutmk:v:40:y:2020:i:5:p:749-760
    DOI: 10.1002/fut.22104
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/fut.22104
    Download Restriction: no
    File URL: https://libkey.io/10.1002/fut.22104?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Scholtus, Martin & van Dijk, Dick & Frijns, Bart, 2014. "Speed, algorithmic trading, and market quality around macroeconomic news announcements," Journal of Banking & Finance, Elsevier, vol. 38(C), pages 89-105.
    2. Frino, Alex & Prodromou, Tina & Wang, George H.K. & Westerholm, P. Joakim & Zheng, Hui, 2017. "An empirical analysis of algorithmic trading around earnings announcements," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 34-51.
    3. Alex Frino & Terry Walter & Andrew West, 2000. "The lead–lag relationship between equities and stock index futures markets around information releases," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(5), pages 467-487, May.
    4. Jean‐François Gajewski, 1999. "Earnings Announcements, Asymmetric Information, Trades and Quotes," European Financial Management, European Financial Management Association, vol. 5(3), pages 411-424, November.
    5. Putniņš, Tālis J., 2013. "What do price discovery metrics really measure?," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 68-83.
    6. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    7. Frino, Alex & Mollica, Vito & Monaco, Eleonora & Palumbo, Riccardo, 2017. "The effect of algorithmic trading on market liquidity: Evidence around earnings announcements on Borsa Italiana," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 82-90.
    8. Lee, Charles M C & Mucklow, Belinda & Ready, Mark J, 1993. "Spreads, Depths, and the Impact of Earnings Information: An Intraday Analysis," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 345-374.
    9. Martin Hauptfleisch & Tālis J. Putniņš & Brian Lucey, 2016. "Who Sets the Price of Gold? London or New York," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(6), pages 564-586, June.
    10. McInish, Thomas H & Wood, Robert A, 1992. "An Analysis of Intraday Patterns in Bid/Ask Spreads for NYSE Stocks," Journal of Finance, American Finance Association, vol. 47(2), pages 753-764, June.
    11. Alex Frino & Vito Mollica & Robert I. Webb, 2014. "The Impact of Co‐Location of Securities Exchanges' and Traders' Computer Servers on Market Liquidity," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(1), pages 20-33, January.
    12. Hasbrouck, Joel, 1995. "One Security, Many Markets: Determining the Contributions to Price Discovery," Journal of Finance, American Finance Association, vol. 50(4), pages 1175-1199, September.
    13. Frino, Alex & Hill, Amelia, 2001. "Intraday futures market behaviour around major scheduled macroeconomic announcements: Australian evidence," Journal of Banking & Finance, Elsevier, vol. 25(7), pages 1319-1337, July.
    14. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    15. Baillie, Richard T. & Geoffrey Booth, G. & Tse, Yiuman & Zabotina, Tatyana, 2002. "Price discovery and common factor models," Journal of Financial Markets, Elsevier, vol. 5(3), pages 309-321, July.
    16. Jonathan Brogaard & Björn Hagströmer & Lars Nordén & Ryan Riordan, 2015. "Trading Fast and Slow: Colocation and Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 28(12), pages 3407-3443.
    17. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    18. Michael J. Fleming & Eli M. Remolona, 1999. "Price Formation and Liquidity in the U.S. Treasury Market: The Response to Public Information," Journal of Finance, American Finance Association, vol. 54(5), pages 1901-1915, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    2. Qixuan Luo & Shijia Song & Handong Li, 2023. "Research on the Effects of Liquidation Strategies in the Multi-asset Artificial Market," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1721-1750, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Frino, Alex & Prodromou, Tina & Wang, George H.K. & Westerholm, P. Joakim & Zheng, Hui, 2017. "An empirical analysis of algorithmic trading around earnings announcements," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 34-51.
    2. Vinay Patel, 2015. "Price Discovery in US and Australian Stock and Options Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 27, July-Dece.
    3. Vinay Patel, 2015. "Price Discovery in US and Australian Stock and Options Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 6-2015.
    4. Ibikunle, Gbenga & Aquilina, Matteo & Diaz-Rainey, Ivan & Sun, Yuxin, 2021. "City goes dark: Dark trading and adverse selection in aggregate markets," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 1-22.
    5. Rzayev, Khaladdin & Ibikunle, Gbenga & Steffen, Tom, 2023. "The market quality implications of speed in cross-platform trading: evidence from Frankfurt-London microwave," LSE Research Online Documents on Economics 119989, London School of Economics and Political Science, LSE Library.
    6. Aggarwal, Nidhi & Panchapagesan, Venkatesh & Thomas, Susan, 2023. "When is the order-to-trade ratio fee effective?," Journal of Financial Markets, Elsevier, vol. 62(C).
    7. Hatch, Brian C. & Johnson, Shane A. & Wang, Qin Emma & Zhang, Jun, 2021. "Algorithmic trading and firm value," Journal of Banking & Finance, Elsevier, vol. 125(C).
    8. Lin, Chu-Bin & Chou, Robin K. & Wang, George H.K., 2018. "Investor sentiment and price discovery: Evidence from the pricing dynamics between the futures and spot markets," Journal of Banking & Finance, Elsevier, vol. 90(C), pages 17-31.
    9. Frino, Alex & Mollica, Vito & Monaco, Eleonora & Palumbo, Riccardo, 2017. "The effect of algorithmic trading on market liquidity: Evidence around earnings announcements on Borsa Italiana," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 82-90.
    10. Dodd, Olga & Frijns, Bart & Indriawan, Ivan & Pascual, Roberto, 2023. "US cross-listing and domestic high-frequency trading: Evidence from Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 301-320.
    11. George Jiang & Ingrid Lo & Giorgio Valente, 2014. "High-Frequency Trading around Macroeconomic News Announcements: Evidence from the U.S. Treasury Market," Staff Working Papers 14-56, Bank of Canada.
    12. Ya‐Kai Chang & Robin K. Chou, 2022. "Algorithmic trading and market quality: Evidence from the Taiwan index futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1837-1855, October.
    13. Zhou, Hao & Kalev, Petko S., 2019. "Algorithmic and high frequency trading in Asia-Pacific, now and the future," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 186-207.
    14. Murray, Hamish & Pham, Thu Phuong & Singh, Harminder, 2016. "Latency reduction and market quality: The case of the Australian Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 257-265.
    15. Hautsch, Nikolaus & Noé, Michael & Zhang, S. Sarah, 2017. "The ambivalent role of high-frequency trading in turbulent market periods," CFS Working Paper Series 580, Center for Financial Studies (CFS).
    16. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    17. Zou, Liping & Rose, Lawrence C. & Pinfold, John F., 2006. "Intra-night trading behaviour of Australian treasury-bond futures overnight options," International Review of Financial Analysis, Elsevier, vol. 15(4-5), pages 415-433.
    18. Breedon, Francis & Chen, Louisa & Ranaldo, Angelo & Vause, Nicholas, 2023. "Judgment day: Algorithmic trading around the Swiss franc cap removal," Journal of International Economics, Elsevier, vol. 140(C).
    19. Ben Ammar, Imen & Hellara, Slaheddine & Ghadhab, Imen, 2020. "High-frequency trading and stock liquidity: An intraday analysis," Research in International Business and Finance, Elsevier, vol. 53(C).
    20. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jfutmk:v:40:y:2020:i:5:p:749-760. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0270-7314/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.