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Strategic Trading in Informationally Complex Environments

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  • Lambert, Nicolas

    (Stanford University)

  • Ostrovsky, Michael

    (Stanford University)

  • Panov, Mikhail

    (Stanford University)

Abstract

We study trading behavior and the properties of prices in informationally complex markets. Our model is based on the single-period version of the linear-normal framework of Kyle (1985). We allow for essentially arbitrary correlations among the random variables involved in the model: the true value of the traded asset, the signals of strategic traders, the signals of competitive market makers, and the demand coming from liquidity traders. We first show that there always exists a unique linear equilibrium, which can be characterized analytically, and illustrate its properties in a series of examples. We then use this equilibrium characterization to study the informational eciency of prices as the number of strategic traders becomes large. If the demand from liquidity traders is uncorrelated with the true value of the asset or is positively correlated with it (conditional on other signals), then prices in large markets aggregate all available information. If, however, the demand from liquidity traders is negatively correlated with the true value of the asset, then prices in large markets aggregate all available information except that contained in liquidity demand.

Suggested Citation

  • Lambert, Nicolas & Ostrovsky, Michael & Panov, Mikhail, 2014. "Strategic Trading in Informationally Complex Environments," Research Papers 3021, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3021
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    Cited by:

    1. Alp E. Atakan & Mehmet Ekmekci, 2021. "Market Selection and the Information Content of Prices," Econometrica, Econometric Society, vol. 89(5), pages 2049-2079, September.
    2. Manzano, Carolina & Vives, Xavier, 2021. "Market power and welfare in asymmetric divisible good auctions," Theoretical Economics, Econometric Society, vol. 16(3), July.
    3. Jihad C. Elnaboulsi & Wassim Daher & Yiğit Sağlam, 2023. "Environmental taxation, information precision, and information sharing," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 25(2), pages 301-341, April.
    4. Dirk Bergemann & Tibor Heumann & Stephen Morris, 2015. "Information and Market Power," Levine's Bibliography 786969000000001101, UCLA Department of Economics.
    5. Guo, Mng, 2023. "Dampening effect and market efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    6. Zhou, Deqing & Wang, Wenjie, 2020. "Insider, outsider and information heterogeneity," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    7. Dai, Shangze & Fan, Fei & Zhang, Keke, 2022. "Creative Destruction and Stock Price Informativeness in Emerging Economies," MPRA Paper 113661, University Library of Munich, Germany.
    8. Wassim Daher & Fida Karam & Naveed Ahmed, 2023. "Insider Trading with Semi-Informed Traders and Information Sharing: The Stackelberg Game," Mathematics, MDPI, vol. 11(22), pages 1-16, November.
    9. Galanis, S. & Ioannou, C. & Kotronis, S., 2019. "Information Aggregation Under Ambiguity: Theory and Experimental Evidence," Working Papers 20/05, Department of Economics, City University London.
    10. Gong, Aibo & Ke, Shaowei & Qiu, Yawen & Shen, Rui, 2022. "Robust pricing under strategic trading," Journal of Economic Theory, Elsevier, vol. 199(C).
    11. Wassim Daher & Harun Aydilek & Elias G. Saleeby, 2020. "Insider trading with different risk attitudes," Journal of Economics, Springer, vol. 131(2), pages 123-147, October.
    12. Marzena Rostek & Ji Hee Yoon, 2021. "Exchange Design and Efficiency," Econometrica, Econometric Society, vol. 89(6), pages 2887-2928, November.
    13. Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2019. "Optimal and Myopic Information Acquisition," Working Papers 2019-25, Princeton University. Economics Department..
    14. Zhou, Deqing & Zhen, Fang, 2021. "Risk aversion, informative noise trading, and long-lived information," Economic Modelling, Elsevier, vol. 97(C), pages 247-254.
    15. Schneider, Julian & Oehler, Andreas, 2021. "Competition for visibility: When do (FX) signal providers employ lotteries?," International Review of Financial Analysis, Elsevier, vol. 78(C).
    16. Bruno Bouchard & Masaaki Fukasawa & Martin Herdegen & Johannes Muhle-Karbe, 2017. "Equilibrium Returns with Transaction Costs," Papers 1707.08464, arXiv.org, revised Apr 2018.
    17. Savitar Sundaresan & Jaromir Nosal & Marcin Kacperczyk, 2017. "Market Power and Informational Efficiency," 2017 Meeting Papers 356, Society for Economic Dynamics.
    18. Sadzik, Tomasz & Woolnough, Chris, 2021. "Snowballing private information," Journal of Economic Theory, Elsevier, vol. 198(C).
    19. Lou, Youcheng & Parsa, Sahar & Ray, Debraj & Li, Duan & Wang, Shouyang, 2019. "Information aggregation in a financial market with general signal structure," Journal of Economic Theory, Elsevier, vol. 183(C), pages 594-624.
    20. Heumann, Tibor, 2021. "Efficiency in trading markets with multi-dimensional signals," Journal of Economic Theory, Elsevier, vol. 191(C).

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    More about this item

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • 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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