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Market Fairness: The Poor Country Cousin of Market Efficiency


  • Michael J. Aitken

    () (Macquarie University)

  • Angelo Aspris

    () (University of Sydney)

  • Sean Foley

    () (University of Sydney)

  • Frederick H. de B. Harris

    () (Wake Forest University)


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

    1. J. Dugast & T. Foucault, 2014. "False News, Informational Efficiency, and Price Reversals," Working papers 513, Banque de France.
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    7. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    8. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
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    1. 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.
    2. 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).

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    More about this item


    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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