IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v223y2014i1p403-43210.1007-s10479-013-1382-8.html
   My bibliography  Save this article

High frequency trading, liquidity, and execution cost

Author

Listed:
  • Edward Sun
  • Timm Kruse
  • Min-Teh Yu

Abstract

We build a model under the framework of discrete optimization to explain how high frequency trading (HFT) can be applied to supply liquidity and reduce execution cost. We derive the analytical properties of our model in finding the optimal solution to minimize the overall execution cost of HFT. We show that the execution cost can be reduced after increasing trading frequency (i.e., the higher the trading frequency, the lower the execution cost) with a simulation study. In addition, we conduct an empirical investigation with tick level data from US equity market through January 2008 to October 2010 to verify our conclusion drawn from the simulation study. Based on the simulation and empirical results we collected, we show that the HFT can reduce the execution cost when supplying liquidity. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Edward Sun & Timm Kruse & Min-Teh Yu, 2014. "High frequency trading, liquidity, and execution cost," Annals of Operations Research, Springer, vol. 223(1), pages 403-432, December.
    • Handle: RePEc:spr:annopr:v:223:y:2014:i:1:p:403-432:10.1007/s10479-013-1382-8
    DOI: 10.1007/s10479-013-1382-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-013-1382-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-013-1382-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lee, Charles M C & Mucklow, Belinda & Ready, Mark J, 1993. "Spreads, Depths, and the Impact of Earnings Information: An Intraday Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 345-374.
    2. Sun, Edward W. & Rezania, Omid & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "Analysis of the intraday effects of economic releases on the currency market," Journal of International Money and Finance, Elsevier, vol. 30(4), pages 692-707, June.
    3. Keim, Donald B. & Madhavan, Ananth, 1995. "Anatomy of the trading process Empirical evidence on the behavior of institutional traders," Journal of Financial Economics, Elsevier, vol. 37(3), pages 371-398, March.
    4. Chan, Louis K C & Lakonishok, Josef, 1995. "The Behavior of Stock Prices around Institutional Trades," Journal of Finance, American Finance Association, vol. 50(4), pages 1147-1174, September.
    5. Obizhaeva, Anna A. & Wang, Jiang, 2013. "Optimal trading strategy and supply/demand dynamics," Journal of Financial Markets, Elsevier, vol. 16(1), pages 1-32.
    6. 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.
    7. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    8. Ellis, Katrina & Michaely, Roni & O'Hara, Maureen, 2000. "The Accuracy of Trade Classification Rules: Evidence from Nasdaq," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(4), pages 529-551, December.
    9. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
    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. Eunju Lee, 2016. "Short selling and market mispricing," Review of Quantitative Finance and Accounting, Springer, vol. 47(3), pages 797-833, October.
    2. Viktor Manahov, 2018. "The rise of the machines in commodities markets: new evidence obtained using Strongly Typed Genetic Programming," Annals of Operations Research, Springer, vol. 260(1), pages 321-352, January.
    3. Edward W. Sun & Timm Kruse & Yi-Ting Chen, 2019. "Stylized algorithmic trading: satisfying the predictive near-term demand of liquidity," Annals of Operations Research, Springer, vol. 281(1), pages 315-347, October.
    4. Yi-Ting Chen & Wan-Ni Lai & Edward W. Sun, 2019. "Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 809-844, August.
    5. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    6. Edward W. Sun & Yu-Jen Wang & Min-Teh Yu, 2018. "Integrated Portfolio Risk Measure: Estimation and Asymptotics of Multivariate Geometric Quantiles," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 627-652, August.
    7. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    8. Yi-Ting Chen & Edward W. Sun & Min-Teh Yu, 2018. "Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 653-684, August.
    9. Miles Kumaresan & Nataša Krejić, 2015. "Optimal trading of algorithmic orders in a liquidity fragmented market place," Annals of Operations Research, Springer, vol. 229(1), pages 521-540, June.
    10. Sun, Edward W. & Chen, Yi-Ting & Yu, Min-Teh, 2015. "Generalized optimal wavelet decomposing algorithm for big financial data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 194-214.
    11. Kao, Yu-Sheng & Chuang, Hwei-Lin & Ku, Yu-Cheng, 2020. "The empirical linkages among market returns, return volatility, and trading volume: Evidence from the S&P 500 VIX Futures," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).

    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. Hatheway, Frank & Kwan, Amy & Zheng, Hui, 2017. "An Empirical Analysis of Market Segmentation on U.S. Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(6), pages 2399-2427, December.
    2. Stenfors, Alexis & Susai, Masayuki, 2021. "Spoofing and pinging in foreign exchange markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).
    3. Ryosuke Ishii, 2009. "Optimal Execution in an Evolutionary Setting," KIER Working Papers 670, Kyoto University, Institute of Economic Research.
    4. Michael J. O'Neill & Geoffrey J. Warren, 2019. "Evaluating fund capacity: issues and methods," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(S1), pages 773-800, April.
    5. Ryosuke Ishii, 2010. "Optimal Execution in a Market with Small Investors," Applied Mathematical Finance, Taylor & Francis Journals, vol. 17(5), pages 431-451.
    6. Alexander, Gordon J. & Peterson, Mark A., 2007. "An analysis of trade-size clustering and its relation to stealth trading," Journal of Financial Economics, Elsevier, vol. 84(2), pages 435-471, May.
    7. J. Doyne Farmer, 2002. "Market force, ecology and evolution," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(5), pages 895-953, November.
    8. Chakrabarty, Bidisha & Pascual, Roberto & Shkilko, Andriy, 2015. "Evaluating trade classification algorithms: Bulk volume classification versus the tick rule and the Lee-Ready algorithm," Journal of Financial Markets, Elsevier, vol. 25(C), pages 52-79.
    9. Giambona, Erasmo & Golec, Joseph, 2010. "Strategic trading in the wrong direction by a large institutional insider," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 1-22, January.
    10. Wei Cui & Anthony Brabazon & Michael O'Neill, 2011. "Dynamic trade execution: a grammatical evolution approach," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 2(1/2), pages 4-31.
    11. Ting, Christopher & Warachka, Mitch & Zhao, Yonggan, 2007. "Optimal liquidation strategies and their implications," Journal of Economic Dynamics and Control, Elsevier, vol. 31(4), pages 1431-1450, April.
    12. Griese, Knut & Kempf, Alexander, 2005. "Liquiditätsdynamik am deutschen Aktienmarkt," CFR Working Papers 05-12, University of Cologne, Centre for Financial Research (CFR).
    13. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    14. Aktas, Osman Ulas & Kryzanowski, Lawrence, 2014. "Market impacts of trades for stocks listed on the Borsa Istanbul," Emerging Markets Review, Elsevier, vol. 20(C), pages 152-175.
    15. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2015, January-A.
    16. Olivier Guéant, 2016. "The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making," Post-Print hal-01393136, HAL.
    17. Obizhaeva, Anna A. & Wang, Jiang, 2013. "Optimal trading strategy and supply/demand dynamics," Journal of Financial Markets, Elsevier, vol. 16(1), pages 1-32.
    18. Blankespoor, Elizabeth & deHaan, Ed & Marinovic, Iván, 2020. "Disclosure processing costs, investors’ information choice, and equity market outcomes: A review," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    19. Murphy Jun Jie Lee, 2013. "The Microstructure of Trading Processes on the Singapore Exchange," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2013, January-A.
    20. Stenfors, Alexis & Susai, Masayuki, 2019. "Liquidity withdrawal in the FX spot market: A cross-country study using high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 59(C), pages 36-57.

    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:spr:annopr:v:223:y:2014:i:1:p:403-432:10.1007/s10479-013-1382-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.