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Market Fairness: The Poor Country Cousin of Market Efficiency

Author

Listed:
  • Michael J. Aitken

    (Macquarie University)

  • Angelo Aspris

    (University of Sydney)

  • Sean Foley

    (University of Sydney)

  • Frederick H. de B. Harris

    (Wake Forest University)

Abstract

Both fairness and efficiency are important considerations in market design and regulation, yet many regulators have neither defined nor measured these concepts. We develop an evidencebased policy framework in which these are both defined and measured using a series of empirical proxies. We then build a systems estimation model to examine the 2003–2011 explosive growth in algorithmic trading (AT) on the London Stock Exchange and NYSE Euronext Paris. Our results show that greater AT is associated with increased transactional efficiency and reduced information leakage in top quintile stocks. For less liquid stocks, manipulation at the close declines. We also document the tradeoff between reduced spreads and increased manipulation or information leakage following the introduction of MiFID1.

Suggested Citation

  • Michael J. Aitken & Angelo Aspris & Sean Foley & Frederick H. de B. Harris, 2018. "Market Fairness: The Poor Country Cousin of Market Efficiency," Journal of Business Ethics, Springer, vol. 147(1), pages 5-23, January.
    • Handle: RePEc:kap:jbuset:v:147:y:2018:i:1:d:10.1007_s10551-015-2964-y
    DOI: 10.1007/s10551-015-2964-y
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    References listed on IDEAS

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    Cited by:

    1. Iryna Veryzhenko & Arthur Jonath & Etienne Harb, 2022. "Non-Value-Added Tax to improve market fairness and quality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
    2. Edward Curran & Jack Hunt & Vito Mollica, 2021. "Single stock futures and their impact on market quality: Be careful what you wish for," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(11), pages 1677-1692, November.
    3. Jianing Zhu & Cunyi Yang, 2022. "Analysis of Stock Market Information Leakage by RDD," Economic Analysis Letters, Anser Press, vol. 1(1), pages 28-33, September.
    4. Agapova, Anna & Madura, Jeff & Volkov, Nikanor, 2020. "Information leakage of ADRs Prior to company issued guidance," Research in International Business and Finance, Elsevier, vol. 54(C).
    5. Syed Qasim Shah & Izlin Ismail & Aidial Rizal bin Shahrin, 2020. "Heterogeneous investors and deterioration of market integrity: an analysis of market manipulation cases," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 30(2), pages 389-403, May.
    6. Khairul Zharif Zaharudin & Martin R. Young & Wei‐Huei Hsu, 2022. "High‐frequency trading: Definition, implications, and controversies," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 75-107, February.
    7. Kemme, David M. & McInish, Thomas H. & Zhang, Jiang, 2022. "Market fairness and efficiency: Evidence from the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 134(C).
    8. Karkowska, Renata & Palczewski, Andrzej, 2023. "Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures," Research in International Business and Finance, Elsevier, vol. 64(C).
    9. Zhang, Jun & Fu, Xiaoming & Morris, Harry, 2019. "Construction of indicator system of regional economic system impact factors based on fractional differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 25-33.

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    More about this item

    Keywords

    Market quality; Market fairness; Manipulation; Information leakage; Algorithmic trading;
    All these keywords.

    JEL classification:

    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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