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Algorithmic Trading, Liquidity, and Price Discovery: An Intraday Analysis of the SPI 200 Futures

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  • Michael Goldstein
  • Tina Viljoen
  • P. Joakim Westerholm
  • Hui Zheng

Abstract

We study the intraday price impact of algorithmic trading (AT) on futures markets. We find that AT exhibits a strong reverse U-shape intraday pattern, and greater AT activity is related to lower effective spreads, higher realized spreads and lower adverse selection risk, which suggests that algorithmic traders strategically enter the market when transaction costs and information asymmetry are lower. AT is associated with an increase in transaction costs in the subsequent intraday period mainly through an increase in the adverse selection risk, and is positively related to both public and private information. Our results strongly suggest that algorithmic traders are informed and contribute to liquidity and price discovery on the futures markets.

Suggested Citation

  • Michael Goldstein & Tina Viljoen & P. Joakim Westerholm & Hui Zheng, 2014. "Algorithmic Trading, Liquidity, and Price Discovery: An Intraday Analysis of the SPI 200 Futures," The Financial Review, Eastern Finance Association, vol. 49(2), pages 245-270, May.
    • Handle: RePEc:bla:finrev:v:49:y:2014:i:2:p:245-270
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    Cited by:

    1. Phiri, Andrew, 2017. "Threshold convergence between the federal fund rate and South African equity returns around the colocation period," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 13(1).
    2. 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).
    3. Manahov, Viktor, 2016. "A note on the relationship between high-frequency trading and latency arbitrage," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 281-296.
    4. Qixuan Luo & Yu Shi & Xuan Zhou & Handong Li, 2021. "Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1025-1049, December.
    5. David Saltiel & Eric Benhamou, 2018. "Trade Selection with Supervised Learning and OCA," Papers 1812.04486, arXiv.org.
    6. Jurich, Stephen N. & Mishra, Ajay Kumar & Parikh, Bhavik, 2020. "Indecisive algos: Do limit order revisions increase market load?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    7. 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.
    8. Zhou, Hao & Kalev, Petko S. & Frino, Alex, 2020. "Algorithmic trading in turbulent markets," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).

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