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Stylized algorithmic trading: satisfying the predictive near-term demand of liquidity

Author

Listed:
  • Edward W. Sun

    (KEDGE Business School)

  • Timm Kruse

    (InfoTech)

  • Yi-Ting Chen

    (College of Computer Science, National Chiao Tung University)

Abstract

Regulatory reform enacted (e.g., the Dodd-Frank Act enforced in the U.S.) requires the financial service industry to consider the “reasonably expected near term demand” (i.e., RENTD) in trading. To manage the price impact and transaction cost associated with orders submitted to an order driven market, market makers or specialists must determine their trading styles (aggressive, neutral, or passive) based on the market liquidity in response to RENTD, particularly for trading a large quantity of some financial instrument. In this article we introduce a model considering different trading styles to satisfy the predictive near-term customer demand of market liquidity in order to find an optimal order submission strategy based on different market situations. We show some analytical properties and numerical performances of our model in search of optimal solutions. We evaluate the performances of our model with simulations run over a set of experiments in comparison with two alternative strategies. Our results suggest that the proposed model illustrates superiority in performance.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:281:y:2019:i:1:d:10.1007_s10479-019-03150-0
    DOI: 10.1007/s10479-019-03150-0
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    References listed on IDEAS

    as
    1. Edward Sun & Timm Kruse & Min-Teh Yu, 2015. "Financial Transaction Tax: Policy Analytics Based on Optimal Trading," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 103-141, June.
    2. Bessembinder, Hendrik & Carrion, Allen & Tuttle, Laura & Venkataraman, Kumar, 2016. "Liquidity, resiliency and market quality around predictable trades: Theory and evidence," Journal of Financial Economics, Elsevier, vol. 121(1), pages 142-166.
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    Cited by:

    1. Barbara Będowska-Sójka, 2021. "Is liquidity wasted? The zero-returns on the Warsaw Stock Exchange," Annals of Operations Research, Springer, vol. 297(1), pages 37-51, February.
    2. Giacomo Morelli, 2021. "Liquidity drops," Annals of Operations Research, Springer, vol. 299(1), pages 711-719, April.

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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