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Do supply and demand drive stock prices?

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  • Carl Hopman

Abstract

In this paper, I find that the imbalance between buy and sell orders explains most of the stock price changes. I then show that this effect is driven largely by uninformed price pressure, and not only by private information. To obtain that result, I first establish that causality goes from orders to price. I then distinguish between private information and uninformed price pressure by looking at the implications of a private information model. For idiosyncratic returns, where one would expect private information to be important and the R2 of return on order flow to be high, the R2 is indeed around 41%. However, for the common market return, where one would expect private information to be minor, the R2 is even higher at 70%. This argues against private information and in favor of uninformed price pressure. Moreover, the 70% of the market return that is generated by the order flow imbalance is too high not to include some transitory components of the market return, as defined in the literature on long-term mean reversion. This means that the order flow temporarily moves stock prices away from their fundamental value. This paper points toward a bigger role for uninformed price pressure than is usually assumed.

Suggested Citation

  • Carl Hopman, 2007. "Do supply and demand drive stock prices?," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 37-53.
  • Handle: RePEc:taf:quantf:v:7:y:2007:i:1:p:37-53
    DOI: 10.1080/14697680600987216
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    Cited by:

    1. Frank McGroarty & Ash Booth & Enrico Gerding & V. L. Raju Chinthalapati, 2019. "High frequency trading strategies, market fragility and price spikes: an agent based model perspective," Annals of Operations Research, Springer, vol. 282(1), pages 217-244, November.
    2. Dinh, Minh Thi Hong, 2018. "The relationship between volume imbalance and spread," Research in International Business and Finance, Elsevier, vol. 44(C), pages 76-87.
    3. Jagjeev Dosanjh, 2017. "Exchange Initiatives and Market Efficiency: Evidence from the Australian Securities Exchange," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2017, January-A.
    4. Baruch, Shmuel & Panayides, Marios & Venkataraman, Kumar, 2017. "Informed trading and price discovery before corporate events," Journal of Financial Economics, Elsevier, vol. 125(3), pages 561-588.
    5. Jean-Philippe Bouchaud, 2021. "The Inelastic Market Hypothesis: A Microstructural Interpretation," Papers 2108.00242, arXiv.org, revised Jan 2022.
    6. J. Doyne Farmer & Austin Gerig & Fabrizio Lillo & Henri Waelbroeck, 2013. "How efficiency shapes market impact," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1743-1758, November.
    7. Kashyap, Ravi, 2020. "David vs Goliath (You against the Markets), A dynamic programming approach to separate the impact and timing of trading costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    8. Olivier Guéant, 2016. "The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making," Post-Print hal-01393136, HAL.
    9. Lublóy, Ágnes & Gyarmati, Ákos & Váradi, Kata, 2012. "Virtuális árhatás a Budapesti Értéktőzsdén [Virtual price effects on the Budapest stock exchange]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 508-539.
    10. Obizhaeva, Anna A. & Wang, Jiang, 2013. "Optimal trading strategy and supply/demand dynamics," Journal of Financial Markets, Elsevier, vol. 16(1), pages 1-32.
    11. Emilio Said, 2022. "Market Impact: Empirical Evidence, Theory and Practice," Working Papers hal-03668669, HAL.
    12. Emilio Said, 2022. "Market Impact: Empirical Evidence, Theory and Practice," Papers 2205.07385, arXiv.org.
    13. repec:uts:finphd:34 is not listed on IDEAS
    14. Gyarmati, Ákos & Lublóy, Ágnes & Váradi, Kata, 2012. "The Budapest liquidity measure and the price impact function," MPRA Paper 40339, University Library of Munich, Germany.
    15. Espen Sirnes & Minh Thi Hong Dinh, 2021. "Tick Size and Price Reversal after Order Imbalance," IJFS, MDPI, vol. 9(2), pages 1-13, March.
    16. Alexandru Mandes, 2014. "Order Placement in a Continuous Double Auction Agent Based Model," MAGKS Papers on Economics 201443, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    17. Kyle Bechler & Michael Ludkovski, 2017. "Order Flows and Limit Order Book Resiliency on the Meso-Scale," Papers 1708.02715, arXiv.org.
    18. Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2010. "The Price Impact of Order Book Events," Papers 1011.6402, arXiv.org, revised Apr 2011.

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