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Strategic Trading in Informationally Complex Environments

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  • Nicolas S. Lambert
  • Michael Ostrovsky
  • Mikhail Panov

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 value of the traded asset, the signals of strategic traders and competitive market makers, and the demand from liquidity traders. We show that there always exists a unique linear equilibrium, characterize it analytically, and illustrate its properties in a series of examples. We then use this characterization to study the informational efficiency of prices as the number of strategic traders becomes large. If liquidity demand is positively correlated (or uncorrelated) with the asset value, then prices in large markets aggregate all available information. If liquidity demand is negatively correlated with the asset value, then prices in large markets aggregate all information except that contained in liquidity demand.

Suggested Citation

  • Nicolas S. Lambert & Michael Ostrovsky & Mikhail Panov, 2014. "Strategic Trading in Informationally Complex Environments," NBER Working Papers 20516, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:20516
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    Cited by:

    1. Bergemann, Dirk & Morris, Stephen & Heumann, Tibor, 2015. "Information and Market Power," CEPR Discussion Papers 10791, C.E.P.R. Discussion Papers.
    2. Guo, Mng, 2023. "Dampening effect and market efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    3. Zhou, Deqing & Wang, Wenjie, 2020. "Insider, outsider and information heterogeneity," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    4. 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.
    5. 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.
    6. Manzano, Carolina & Vives, Xavier, 2021. "Market power and welfare in asymmetric divisible good auctions," Theoretical Economics, Econometric Society, vol. 16(3), July.
    7. Schneider, Julian & Oehler, Andreas, 2021. "Competition for visibility: When do (FX) signal providers employ lotteries?," International Review of Financial Analysis, Elsevier, vol. 78(C).
    8. Savitar Sundaresan & Jaromir Nosal & Marcin Kacperczyk, 2017. "Market Power and Informational Efficiency," 2017 Meeting Papers 356, Society for Economic Dynamics.
    9. 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.
    10. Alp E. Atakan & Mehmet Ekmekci, 2021. "Market Selection and the Information Content of Prices," Econometrica, Econometric Society, vol. 89(5), pages 2049-2079, September.
    11. 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.
    12. Dai, Shangze & Fan, Fei & Zhang, Keke, 2022. "Creative Destruction and Stock Price Informativeness in Emerging Economies," MPRA Paper 113661, University Library of Munich, Germany.
    13. Gong, Aibo & Ke, Shaowei & Qiu, Yawen & Shen, Rui, 2022. "Robust pricing under strategic trading," Journal of Economic Theory, Elsevier, vol. 199(C).
    14. 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.
    15. Marzena Rostek & Ji Hee Yoon, 2021. "Exchange Design and Efficiency," Econometrica, Econometric Society, vol. 89(6), pages 2887-2928, November.
    16. Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2019. "Optimal and Myopic Information Acquisition," Working Papers 2019-25, Princeton University. Economics Department..
    17. Zhou, Deqing & Zhen, Fang, 2021. "Risk aversion, informative noise trading, and long-lived information," Economic Modelling, Elsevier, vol. 97(C), pages 247-254.
    18. Bruno Bouchard & Masaaki Fukasawa & Martin Herdegen & Johannes Muhle-Karbe, 2017. "Equilibrium Returns with Transaction Costs," Papers 1707.08464, arXiv.org, revised Apr 2018.
    19. Sadzik, Tomasz & Woolnough, Chris, 2021. "Snowballing private information," Journal of Economic Theory, Elsevier, vol. 198(C).
    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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