IDEAS home Printed from https://ideas.repec.org/a/bla/jfinan/v64y2009i6p2783-2805.html
   My bibliography  Save this article

A Rational Expectations Equilibrium with Informative Trading Volume

Author

Listed:
  • JAN SCHNEIDER

Abstract

A large number of empirical studies find that trading volume contains information about the distribution of future returns. While these studies indicate that observing volume is helpful to an outside observer of the economy it is not clear how investors within the economy can learn from trading volume. In this paper, I show how trading volume helps investors to evaluate the precision of the aggregate information in the price. I construct a model that offers a closed‐form solution of a rational expectations equilibrium where all investors learn from (1) private signals, (2) the market price, and (3) aggregate trading volume.

Suggested Citation

  • Jan Schneider, 2009. "A Rational Expectations Equilibrium with Informative Trading Volume," Journal of Finance, American Finance Association, vol. 64(6), pages 2783-2805, December.
    • Handle: RePEc:bla:jfinan:v:64:y:2009:i:6:p:2783-2805
    DOI: 10.1111/j.1540-6261.2009.01517.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1540-6261.2009.01517.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1540-6261.2009.01517.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bond, Philip & Eraslan, Hülya, 2010. "Information-based trade," Journal of Economic Theory, Elsevier, vol. 145(5), pages 1675-1703, September.
    2. Xiao Li & Linda Du, 2023. "Bitcoin daily price prediction through understanding blockchain transaction pattern with machine learning methods," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-24, January.
    3. Luo, Dan & Mao, Yipeng, 2021. "Fundamental volatility and informative trading volume in a rational expectations equilibrium," Economic Modelling, Elsevier, vol. 105(C).
    4. Hau, Liya & Zhu, Huiming & Shahbaz, Muhammad & Sun, Wuqin, 2021. "Does transaction activity predict Bitcoin returns? Evidence from quantile-on-quantile analysis," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    5. Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled, 2022. "Dynamic dependence and predictability between volume and return of Non-Fungible Tokens (NFTs): The roles of market factors and geopolitical risks," Finance Research Letters, Elsevier, vol. 50(C).
    6. Wang, Zijun, 2021. "The high volume return premium and economic fundamentals," Journal of Financial Economics, Elsevier, vol. 140(1), pages 325-345.
    7. Jean-Edouard Colliard, 2017. "Catching Falling Knives: Speculating on Liquidity Shocks," Management Science, INFORMS, vol. 63(8), pages 2573-2591, August.
    8. Ngene, Geoffrey M. & Mungai, Ann Nduati, 2022. "Stock returns, trading volume, and volatility: The case of African stock markets," International Review of Financial Analysis, Elsevier, vol. 82(C).
    9. Gebka, Bartosz & Wohar, Mark E., 2013. "Causality between trading volume and returns: Evidence from quantile regressions," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 144-159.
    10. Gupta, Suman & Das, Debojyoti & Hasim, Haslifah & Tiwari, Aviral Kumar, 2018. "The dynamic relationship between stock returns and trading volume revisited: A MODWT-VAR approach," Finance Research Letters, Elsevier, vol. 27(C), pages 91-98.
    11. Aris Kartsaklas, 2018. "Trader Type Effects On The Volatility‐Volume Relationship Evidence From The Kospi 200 Index Futures Market," Bulletin of Economic Research, Wiley Blackwell, vol. 70(3), pages 226-250, July.
    12. Zhou, Deqing & Wang, Wenjie, 2020. "Insider, outsider and information heterogeneity," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    13. Condie, Scott & Ganguli, Jayant, 2017. "The pricing effects of ambiguous private information," Journal of Economic Theory, Elsevier, vol. 172(C), pages 512-557.
    14. repec:esx:essedp:720 is not listed on IDEAS
    15. Eduardo Dávila & Cecilia Parlatore, 2021. "Trading Costs and Informational Efficiency," Journal of Finance, American Finance Association, vol. 76(3), pages 1471-1539, June.
    16. Elina Pradkhan, 2016. "Information Content of Trading Activity in Precious Metals Futures Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(5), pages 421-456, May.
    17. Michael Sockin & Wei Xiong, 2020. "A Model of Cryptocurrencies," NBER Working Papers 26816, National Bureau of Economic Research, Inc.
    18. Emiliano Pagnotta, 2016. "Chasing Private Information," 2016 Meeting Papers 1673, Society for Economic Dynamics.
    19. M. Kannadhasan & Pankaj Singh & Parikshit Charan & Pavan Kumar Balivada, 2018. "Personality characteristics and the process of start-up: the moderating role of institutional environment," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 45(4), pages 287-300, December.
    20. Ding, Jing & Fang, Libing & Chen, Shi, 2020. "Mitigating free riding in social networks: The impact of underestimating others’ ability in financial market," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 582-599.
    21. 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.
    22. Kim, Junwoo & Kim, Robert & Kim, Sangwan, 2020. "Does financial statement comparability mitigate delayed trading volume before earnings announcements?," Journal of Business Research, Elsevier, vol. 107(C), pages 62-75.
    23. Bing Han & Liyan Yang, 2013. "Social Networks, Information Acquisition, and Asset Prices," Management Science, INFORMS, vol. 59(6), pages 1444-1457, June.
    24. Ganguli, Jayant & Condie, Scott, 2012. "The pricing effects of ambiguous private information," Economics Discussion Papers 5631, University of Essex, Department of Economics.
    25. Iuliia Brushko & Stephen P. Ferris & Jan Hanousek & Jiri Tresl, 2020. "Intra-Industry Transfer of Information Inferred From Trading Volume," CERGE-EI Working Papers wp663, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jfinan:v:64:y:2009:i:6:p:2783-2805. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/afaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.