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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

    as
    1. J. Dugast & T. Foucault, 2014. "False News, Informational Efficiency, and Price Reversals," Working papers 513, Banque de France.
    2. Hendershott, Terrence & Riordan, Ryan, 2013. "Algorithmic Trading and the Market for Liquidity," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1001-1024, August.
    3. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    4. Thomas H. McInish & James Upson, 2013. "The Quote Exception Rule: Giving High Frequency Traders an Unintended Advantage," Financial Management, Financial Management Association International, vol. 42(3), pages 481-501, September.
    5. Hendershott, Terrence & Moulton, Pamela C., 2011. "Automation, speed, and stock market quality: The NYSE's Hybrid," Journal of Financial Markets, Elsevier, vol. 14(4), pages 568-604, November.
    6. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    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.
    9. Yan, Bingcheng & Zivot, Eric, 2010. "A structural analysis of price discovery measures," Journal of Financial Markets, Elsevier, vol. 13(1), pages 1-19, February.
    10. Albert S. Kyle & S. Viswanathan, 2008. "How to Define Illegal Price Manipulation," American Economic Review, American Economic Association, vol. 98(2), pages 274-279, May.
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    Citations

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    Cited by:

    1. 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).
    2. 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.

    More about this item

    Keywords

    Market quality; Market fairness; Manipulation; Information leakage; Algorithmic trading;

    JEL classification:

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

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