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Algorithmic trading for online portfolio selection under limited market liquidity

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  • Ha, Youngmin
  • Zhang, Hai

Abstract

We propose an optimal intraday trading algorithm to reduce overall transaction costs by absorbing price shocks when an online portfolio selection (OPS) method rebalances its portfolio. Having considered the real-time data of limit order books (LOBs), the trading algorithm optimally splits a sizeable market order into a number of consecutive market orders to minimise the overall transaction costs, including both the liquidity costs and the proportional transaction costs. The proposed trading algorithm, compatible with any OPS methods, optimises the number of intraday trades and finds an optimal intraday trading path. Backtesting results from the historical LOB data of NASDAQ-traded stocks show that the proposed trading algorithm significantly reduces the overall transaction costs when market liquidity is limited.

Suggested Citation

  • Ha, Youngmin & Zhang, Hai, 2020. "Algorithmic trading for online portfolio selection under limited market liquidity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1033-1051.
    • Handle: RePEc:eee:ejores:v:286:y:2020:i:3:p:1033-1051
    DOI: 10.1016/j.ejor.2020.03.050
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    References listed on IDEAS

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    Cited by:

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    2. Guo, Sini & Gu, Jia-Wen & Ching, Wai-Ki, 2021. "Adaptive online portfolio selection with transaction costs," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1074-1086.
    3. Chun-Hao Chen & Yu-Hsuan Chen & Vicente Garcia Diaz & Jerry Chun-Wei Lin, 2023. "RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm," Electronic Commerce Research, Springer, vol. 23(1), pages 3-42, March.
    4. Schnaubelt, Matthias, 2022. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," European Journal of Operational Research, Elsevier, vol. 296(3), pages 993-1006.
    5. Guo, Sini & Gu, Jia-Wen & Fok, Christopher H. & Ching, Wai-Ki, 2023. "Online portfolio selection with state-dependent price estimators and transaction costs," European Journal of Operational Research, Elsevier, vol. 311(1), pages 333-353.
    6. Sadoghi, Amirhossein & Vecer, Jan, 2022. "Optimal liquidation problem in illiquid markets," European Journal of Operational Research, Elsevier, vol. 296(3), pages 1050-1066.
    7. Amirhossein Sadoghi & Jan Vecer, 2022. "Optimal liquidation problem in illiquid markets," Post-Print hal-03696768, HAL.
    8. Jin’an He & Fangping Peng & Xiuying Xie, 2024. "Risk-adjusted exponential gradient strategies for online portfolio selection," Journal of Combinatorial Optimization, Springer, vol. 48(1), pages 1-25, August.
    9. Zhong, Yannan & Xu, Weijun & Li, Hongyi & Zhong, Weiwei, 2024. "Distributed mean reversion online portfolio strategy with stock network," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1143-1158.
    10. Weiwei Guo & Wei-Guo Zhang & Zaiwu Gong, 2024. "Modeling of linear uncertain portfolio selection with uncertain constraint and risk index," Fuzzy Optimization and Decision Making, Springer, vol. 23(3), pages 469-496, September.

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