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Price, Trade Size, and Information Revelation in Multi-Period Securities Markets

Author

Listed:
  • Han N. Ozsoylev
  • Shino Takayama
  • The University of Sydney

Abstract

We study price formation in securities markets, using the sequential trade framework of Glosten and Milgrom [7]. This paper makes one basic methodological advance over previous research on sequential securities trading: we allow traders to choose from n trade sizes in a multi-period market, where n can be arbitrarily large. We examine how trade size multiplicity affects the intertemporal dynamics of trading strategies, bid-ask spreads, and information revelation We show that price impact, as a function of trade size, is increasing and exhibits (discrete) concavity.

Suggested Citation

  • Han N. Ozsoylev & Shino Takayama & The University of Sydney, 2005. "Price, Trade Size, and Information Revelation in Multi-Period Securities Markets," Economics Series Working Papers 2005-FE-10, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:2005-fe-10
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    1. is not listed on IDEAS
    2. Ignacio Arango & Diego A. Agudelo, 2017. "How does information disclosure affect liquidity?Evidence from an Emerging Market," Documentos de Trabajo de Valor Público 16990, Universidad EAFIT.
    3. Shino Takayama, 2013. "Price Manipulation, Dynamic Informed Trading and Tame Equilibria: Theory and Computation," Discussion Papers Series 492, School of Economics, University of Queensland, Australia.
    4. Takayama, Shino, 2021. "Price manipulation, dynamic informed trading, and the uniqueness of equilibrium in sequential trading," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
    5. Diego A. Agudelo & Ignacio Arango, 2017. "How does information disclosure affect liquidity? Evidence from an Emerging Market," Documentos de Trabajo de Valor Público 16944, Universidad EAFIT.
    6. Arango, Ignacio & Agudelo, Diego A., 2019. "How does information disclosure affect liquidity? Evidence from an emerging market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    7. Shino Takayama, 2018. "Price Manipulation, Dynamic Informed Trading and Tame Equilibria: Theory and Computation," Discussion Papers Series 603, School of Economics, University of Queensland, Australia.
    8. Louhichi, Waël, 2011. "What drives the volume-volatility relationship on Euronext Paris?," International Review of Financial Analysis, Elsevier, vol. 20(4), pages 200-206, August.
    9. Nihad Aliyev, 2019. "Financial Markets with Multidimensional Uncertainty," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2019, January-A.
    10. Zhou, Tong, 2021. "Ambiguity, asset illiquidity, and price variability," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 280-292.
    11. Nihad Aliyev & Xue-Zhong He, 2017. "Ambiguous Market Making," Research Paper Series 383, Quantitative Finance Research Centre, University of Technology, Sydney.
    12. Malinova, Katya & Park, Andreas, 2013. "Liquidity, volume and price efficiency: The impact of order vs. quote driven trading," Journal of Financial Markets, Elsevier, vol. 16(1), pages 104-126.
    13. Junqian Li & Yuqing Liu & Nhan Buu Phan & Shino Takayama, 2023. "An Experimental Analysis of Dynamic Informed Trading," Discussion Papers Series 665, School of Economics, University of Queensland, Australia.
    14. Katya Malinova & Andreas Park, 2009. "Liquidity, Volume, and Price Behavior: The Impact of Order vs. Quote Based Trading," Working Papers tecipa-358, University of Toronto, Department of Economics.

    More about this item

    Keywords

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    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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