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Informed trading, market efficiency and volatility

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  • Sung, Ming-Chien
  • Johnson, Johnnie E.V.
  • McDonald, David C.J.

Abstract

We establish relationships that have proved difficult to capture in financial markets, between informed trading, efficiency and volatility. We examine the efficiency and volatility of market prices in 6058 parallel horserace betting exchange and bookmaker markets (1.8 million price points). We find that informed trading is associated with increased efficiency and volatility.

Suggested Citation

  • Sung, Ming-Chien & Johnson, Johnnie E.V. & McDonald, David C.J., 2016. "Informed trading, market efficiency and volatility," Economics Letters, Elsevier, vol. 149(C), pages 56-59.
    • Handle: RePEc:eee:ecolet:v:149:y:2016:i:c:p:56-59
    DOI: 10.1016/j.econlet.2016.10.015
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    References listed on IDEAS

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    1. Marshall Gramm & Douglas H. Owens, 2006. "Efficiency in Pari-Mutuel Betting Markets across Wagering Pools in the Simulcast Era," Southern Economic Journal, John Wiley & Sons, vol. 72(4), pages 926-937, April.
    2. Robert Bloomfield & Maureen O'Hara & Gideon Saar, 2009. "How Noise Trading Affects Markets: An Experimental Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 22(6), pages 2275-2302, June.
    3. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
    4. Michael A. Smith & David Paton & Leighton Vaughan Williams, 2006. "Market Efficiency in Person‐to‐Person Betting," Economica, London School of Economics and Political Science, vol. 73(292), pages 673-689, November.
    5. Johnnie E. V. Johnson & Owen Jones & Leilei Tang, 2006. "Exploring Decision Makers' Use of Price Information in a Speculative Market," Management Science, INFORMS, vol. 52(6), pages 897-908, June.
    6. Shin, Hyun Song, 1993. "Measuring the Incidence of Insider Trading in a Market for State-Contingent Claims," Economic Journal, Royal Economic Society, vol. 103(420), pages 1141-1153, September.
    7. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    8. Tavakoli, Manouchehr & McMillan, David & McKnight, Phillip J., 2012. "Insider trading and stock prices," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 254-266.
    9. David Johnstone, 2016. "The Effect of Information on Uncertainty and the Cost of Capital," Contemporary Accounting Research, John Wiley & Sons, vol. 33(2), pages 752-774, June.
    10. Shiller, Robert J, 1990. "Market Volatility and Investor Behavior," American Economic Review, American Economic Association, vol. 80(2), pages 58-62, May.
    11. repec:bla:econom:v:69:y:2002:i:274:p:327-38 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Chung, Kee H. & Chuwonganant, Chairat, 2023. "COVID-19 pandemic and the stock market: Liquidity, price efficiency, and trading," Journal of Financial Markets, Elsevier, vol. 64(C).
    2. Ryu, Doojin & Yang, Heejin, 2017. "Price disagreements and adjustments in index derivatives markets," Economics Letters, Elsevier, vol. 151(C), pages 104-106.
    3. Kee H. Chung & Chairat Chuwonganant, 2023. "Tick size and price efficiency: Further evidence from the Tick Size Pilot Program," Financial Management, Financial Management Association International, vol. 52(3), pages 483-511, September.
    4. Kumar, Manish & Kumar, Arun, 2017. "Performance assessment and degradation analysis of solar photovoltaic technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 554-587.
    5. Moore, Megan & Cristofalo, Margaret & Dotolo, Danae & Torres, Nicole & Lahdya, Alexandra & Ho, Leyna & Vogel, Mia & Forrester, Mollie & Conley, Bonnie & Fouts, Susan, 2017. "When high pressure, system constraints, and a social justice mission collide: A socio-structural analysis of emergency department social work services," Social Science & Medicine, Elsevier, vol. 178(C), pages 104-114.
    6. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.
    7. Zhao, Wandi & Gao, Yang, 2023. "Network connectedness and the contagion structure of informed trading: Evidence from the time and frequency domains," International Review of Financial Analysis, Elsevier, vol. 90(C).
    8. Yayun Shen & Michael Faure, 0. "Green building in China," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 0, pages 1-17.
    9. Tadgh Hegarty, 2021. "Information and price efficiency in the absence of home crowd advantage," Applied Economics Letters, Taylor & Francis Journals, vol. 28(21), pages 1902-1907, December.

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    More about this item

    Keywords

    Informed trading; Efficiency; Volatility;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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