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Trading Volume and Information Revelation in Stock Market

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  • Suominen, Matti

Abstract

I consider a market microstructure model in which the rates of public and private information arrival are probabilistic. The latter depends on the availability of private information that is stochastically changing over time. In equilibrium, traders estimate the availability of private information using past priods' trading volume and use this information to adjust their strategies. The time-series properties include contemporaneous correlation between price variability and volume and autocorrelation in price variability (similar to GARCH). The model explains why trading volume contains useful information for predicting volatility and provides predictions on the limit and market order placement strategies of traders.

Suggested Citation

  • Suominen, Matti, 2001. "Trading Volume and Information Revelation in Stock Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 36(04), pages 545-565, December.
  • Handle: RePEc:cup:jfinqa:v:36:y:2001:i:04:p:545-565_00
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    Cited by:

    1. Cuadro-Sáez, Lucía & Moreno, Manuel, 2007. "GARCH modeling of robust market returns," Kiel Advanced Studies Working Papers 440, Kiel Institute for the World Economy (IfW).
    2. Chuang, Chia-Chang & Kuan, Chung-Ming & Lin, Hsin-Yi, 2009. "Causality in quantiles and dynamic stock return-volume relations," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1351-1360, July.
    3. Thusitha Mahipala & Howard Chan & Robert Faff, 2009. "Trading volume and information asymmetry: routine versus nonroutine earnings announcements in Australia," Applied Financial Economics, Taylor & Francis Journals, vol. 19(21), pages 1737-1752.
    4. Juan Gabriel Brida & W. Adrian Risso, 2009. "Dynamic and Structure of the Italian stock market based on returns and volume trading," Economics Bulletin, AccessEcon, vol. 29(3), pages 2417-2423.
    5. Gebka, Bartosz, 2006. "Leaders and Laggards: International Evidence on Spillovers in Returns, Variance, and Trading Volume," Working Paper Series 2006,1, European University Viadrina Frankfurt (Oder), The Postgraduate Research Programme Capital Markets and Finance in the Enlarged Europe.
    6. Elezovic, Suad, 2009. "Functional modelling of volatility in the Swedish limit order book," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2107-2118, April.
    7. repec:nbp:nbpbik:v:48:y:2017:i:4:p:375-402 is not listed on IDEAS
    8. M. Illueca & J. A. LaFuente, 2006. "New evidence on expiration‐day effects using realized volatility: An intraday analysis for the Spanish stock exchange," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(9), pages 923-938, September.
    9. Xiang, Ju & Zhu, Xiaoneng, 2014. "Intraday asymmetric liquidity and asymmetric volatility in FTSE-100 futures market," Journal of Empirical Finance, Elsevier, vol. 25(C), pages 134-148.
    10. Ólan T. Henry & Michael McKenzie, 2006. "The Impact of Short Selling on the Price-Volume Relationship: Evidence from Hong Kong," The Journal of Business, University of Chicago Press, vol. 79(2), pages 671-692, March.
    11. Hussain, Syed Mujahid, 2011. "Intraday trading volume and international spillover effects," Research in International Business and Finance, Elsevier, vol. 25(2), pages 183-194, June.
    12. Jia-Wen Gu & Mogens Steffensen, 2015. "Optimal Portfolio Liquidation and Dynamic Mean-variance Criterion," Papers 1510.09110, arXiv.org.
    13. Paul D. McNelis & Carrie K.C. Chan, 2004. "Deflationary Dynamics in Hong Kong: Evidence from Linear and Neural Network Regime Switching Models," Working Papers 212004, Hong Kong Institute for Monetary Research.
    14. repec:eee:jocoma:v:5:y:2017:i:c:p:36-49 is not listed on IDEAS
    15. Ding, Mingfa, 2014. "Political Connections and Stock Liquidity: Political Network, Hierarchy and Intervention," Knut Wicksell Working Paper Series 2014/7, Lund University, Knut Wicksell Centre for Financial Studies.
    16. Rubia, Antonio & Sanchis-Marco, Lidia, 2013. "On downside risk predictability through liquidity and trading activity: A dynamic quantile approach," International Journal of Forecasting, Elsevier, vol. 29(1), pages 202-219.
    17. C. Y. Yiu & K. F. Man & S. K. Wong, 2008. "Trading Volume and Price Dispersion in Housing Markets," Journal of Property Research, Taylor & Francis Journals, vol. 25(3), pages 203-219, December.
    18. Moonsoo Kang & Kiseok Nam, 2015. "Informed trade and idiosyncratic return variation," Review of Quantitative Finance and Accounting, Springer, vol. 44(3), pages 551-572, April.
    19. Henryk Gurgul & Roland Mestel & Tomasz Wojtowicz, 2007. "Distribution of volume on the American stock market," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 1, pages 143-163.
    20. Hsieh, Shu-Fan, 2013. "Individual and institutional herding and the impact on stock returns: Evidence from Taiwan stock market," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 175-188.
    21. Kremer, Stephanie & Nautz, Dieter, 2013. "Causes and consequences of short-term institutional herding," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1676-1686.
    22. Ahmed, Walid M.A., 2017. "The impact of foreign equity flows on market volatility during politically tranquil and turbulent times: The Egyptian experience," Research in International Business and Finance, Elsevier, vol. 40(C), pages 61-77.
    23. Bartosz Gębka, 2012. "The Dynamic Relation Between Returns, Trading Volume, And Volatility: Lessons From Spillovers Between Asia And The United States," Bulletin of Economic Research, Wiley Blackwell, vol. 64(1), pages 65-90, January.
    24. Karpouzis, Efstathios & Bouras, Chris & Kanas, Angelos, 2019. "Hedge fund activism, voice, and value creation," MPRA Paper 92576, University Library of Munich, Germany.

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