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Strategic Trading and Learning About Liquidity

Author

Listed:
  • Harrison Hong
  • Sven Rady

Abstract

Many practitioners point out that the speculative profits of institutional traders are eroded by the difficulty in gauging the price impact of their trades. In this paper, we develop a model of strategic trading where speculators face such a dilemma because of incomplete information about time-varying market liquidity. Unlike the competitive market makers that they trade against, informed traders do not know whether the liquidity (¶noise¶) trades are generated from a distribution with high or low variance. Instead, they have to learn about liquidity from past prices and trading volume. Extreme price deviations from forecasts of fundamentals based on public news or low trading volume tend to lead to revisions of beliefs in favor of the low liquidity state. This revision in beliefs implies that strategic trades and market statistics such as informational efficiency are path-dependent on past market outcomes. Our paper has a number of normative implications for practitioners concerned with gauging the potential price impact of their trades.

Suggested Citation

  • Harrison Hong & Sven Rady, 2000. "Strategic Trading and Learning About Liquidity," FMG Discussion Papers dp356, Financial Markets Group.
    • Handle: RePEc:fmg:fmgdps:dp356
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    Cited by:

    1. is not listed on IDEAS
    2. Jianhao Su & Yanliang Zhang, 2025. "The disclosure of information about the range of asset value in market," Papers 2511.11405, arXiv.org, revised Jan 2026.
    3. Julia M. Puaschunder, 2023. "Finance Followership," RAIS Conference Proceedings 2022-2025 0249, Research Association for Interdisciplinary Studies.
    4. Nam, Jouahn & Wang, Jun & Zhang, Ge, 2008. "Strategic trading against retail investors with loss-aversion," International Review of Economics & Finance, Elsevier, vol. 17(1), pages 45-55.
    5. Acharya, Viral V. & Johnson, Timothy C., 2007. "Insider trading in credit derivatives," Journal of Financial Economics, Elsevier, vol. 84(1), pages 110-141, April.
    6. Marmora, Paul & Rytchkov, Oleg, 2018. "Learning about noise," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 209-224.
    7. Alavi, Soogand & Haruvy, Ernan & Xie, Ying, 2025. "On the role of provenance in NFT trades," International Journal of Research in Marketing, Elsevier, vol. 42(3), pages 610-625.
    8. Mu-Shun Wang, 2022. "Shareholder Disputes and Commonality in Liquidity: Evidence from the Equity Markets in China," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(2), pages 291-325, June.

    More about this item

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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