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High frequency trading, liquidity, and execution cost

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  • 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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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).

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