IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpfi/0207017.html
   My bibliography  Save this paper

Time-Varying Arrival Rates of Informed and Uninformed Trades

Author

Listed:
  • David Easley

    (Cornell University)

  • Robert F. Engle

    (New York University)

  • Maureen O'Hara

    (Cornell University)

  • Liuren Wu

    (Fordham University)

Abstract

In this paper we extend the model of Easley and O'Hara (1992) to allow the arrival rates of informed and uninformed trades to be time-varying and forecastable. We specify a generalized autoregressive bivariate process for the arrival rates of informed and uninformed trades and estimate the model on 16 actively traded stocks on the New York Stock Exchange over 15 years of transaction data. Our results show that uninformed trades are highly persistent. Uninformed order arrivals clump together, with high uninformed volume days likely to follow high uninformed volume days, and conversely. This behavior is consistent with the passive characterization of the uninformed found in the literature. But we do find an important difference in how the uninformed behave; they avoid trading when the informed are forecasted to be present. Informed trades also exhibit complex patterns, but these patterns are not consistent with the strategic behavior posited in the literature. The informed do not appear to hide in order flow, but instead they trade persistently. We also investigate the correlation between the arrival rates of trades and trade composition on market volatility, liquidity and depth. We find that although volatility increases with the forecasted arrival rates of total trades, it is relatively independent of the forecasted composition of the trade. We use the opening bid-ask spread as a measure of market liquidity. We find that as the number of trades increases over time, the relative proportion of informed trades decreases and hence, spreads become narrower and the market becomes more liquid. Finally, we compute the price impact curve of consecutive buy orders and report the half life of the price impact as a measure of market depth. We find a positive correlation between the half life and total trades indicating that the market is deeper in presence of more trades.

Suggested Citation

  • David Easley & Robert F. Engle & Maureen O'Hara & Liuren Wu, 2002. "Time-Varying Arrival Rates of Informed and Uninformed Trades," Finance 0207017, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpfi:0207017
    Note: Type of Document - pdf; prepared on LaTex; to print on postscript; pages: 38 ; figures: included. prepared via dvipdfm
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/fin/papers/0207/0207017.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Foster, F Douglas & Viswanathan, S, 1990. "A Theory of the Interday Variations in Volume, Variance, and Trading Costs in Securities Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 593-624.
    3. Chordia, Tarun & Subrahmanyam, Avanidhar & Anshuman, V. Ravi, 2001. "Trading activity and expected stock returns," Journal of Financial Economics, Elsevier, vol. 59(1), pages 3-32, January.
    4. Alfonso Dufour & Robert F. Engle, 2000. "Time and the Price Impact of a Trade," Journal of Finance, American Finance Association, vol. 55(6), pages 2467-2498, December.
    5. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
    6. Easley, David & Kiefer, Nicholas M & O'Hara, Maureen, 1997. "One Day in the Life of a Very Common Stock," The Review of Financial Studies, Society for Financial Studies, vol. 10(3), pages 805-835.
    7. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    8. Anat R. Admati, Paul Pfleiderer, 1988. "A Theory of Intraday Patterns: Volume and Price Variability," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 3-40.
    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    10. Engle, Robert F. & Lange, Joe, 2001. "Predicting VNET: A model of the dynamics of market depth," Journal of Financial Markets, Elsevier, vol. 4(2), pages 113-142, April.
    11. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    12. Easley, David & Kiefer, Nicholas M & O'Hara, Maureen, 1996. "Cream-Skimming or Profit-Sharing? The Curious Role of Purchased Order Flow," Journal of Finance, American Finance Association, vol. 51(3), pages 811-833, July.
    13. Kerry Back & C. Henry Cao & Gregory A. Willard, 2000. "Imperfect Competition among Informed Traders," Journal of Finance, American Finance Association, vol. 55(5), pages 2117-2155, October.
    14. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    15. Easley, David & O'Hara, Maureen, 1992. "Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Joey Wenling, 2011. "Transaction duration and asymmetric price impact of trades--Evidence from Australia," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 91-102, January.
    2. repec:bla:jecsur:v:22:y:2008:i:4:p:711-751 is not listed on IDEAS
    3. Wong, Woon K. & Tan, Dijun & Tian, Yixiang, 2009. "Informed trading and liquidity in the Shanghai Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 66-73, March.
    4. Manganelli, Simone, 2005. "Duration, volume and volatility impact of trades," Journal of Financial Markets, Elsevier, vol. 8(4), pages 377-399, November.
    5. N. Taylor & Y. Xu, 2017. "The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 1021-1035, July.
    6. Berkman, Henk & Koch, Paul D., 2008. "Noise trading and the price formation process," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 232-250, March.
    7. Grammig, Joachim & Theissen, Erik & Wuensche, Oliver, 2007. "Time and price impact of a trade: A structural approach," CFR Working Papers 07-12, University of Cologne, Centre for Financial Research (CFR).
    8. Chen, Tao & Li, Jie & Cai, Jun, 2008. "Information content of inter-trade time on the Chinese market," Emerging Markets Review, Elsevier, vol. 9(3), pages 174-193, September.
    9. Jun (Tony) Ruan & Tongshu Ma, 2017. "Bid-Ask Spread, Quoted Depths, and Unexpected Duration Between Trades," Journal of Financial Services Research, Springer;Western Finance Association, vol. 51(3), pages 385-436, June.
    10. Biais, Bruno & Glosten, Larry & Spatt, Chester, 2005. "Market microstructure: A survey of microfoundations, empirical results, and policy implications," Journal of Financial Markets, Elsevier, vol. 8(2), pages 217-264, May.
    11. Pham, Manh Cuong & Anderson, Heather Margot & Duong, Huu Nhan & Lajbcygier, Paul, 2020. "The effects of trade size and market depth on immediate price impact in a limit order book market," Journal of Economic Dynamics and Control, Elsevier, vol. 120(C).
    12. Alfonso Dufour & Robert F. Engle, 2000. "Time and the Price Impact of a Trade," Journal of Finance, American Finance Association, vol. 55(6), pages 2467-2498, December.
    13. Sun, Yuxin & Ibikunle, Gbenga, 2017. "Informed trading and the price impact of block trades: A high frequency trading analysis," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 114-129.
    14. Bernhardt, Dan & Hughson, Eric, 2002. "Intraday trade in dealership markets," European Economic Review, Elsevier, vol. 46(9), pages 1697-1732, October.
    15. Mazza, Paolo, 2015. "Price dynamics and market liquidity: An intraday event study on Euronext," The Quarterly Review of Economics and Finance, Elsevier, vol. 56(C), pages 139-153.
    16. Chang, Chuang-Chang & Hsieh, Pei-Fang & Lai, Hung-Neng, 2013. "The price impact of options and futures volume in after-hours stock market trading," Pacific-Basin Finance Journal, Elsevier, vol. 21(1), pages 984-1007.
    17. Dennis J. Whalen & Charles D. Collver, 2004. "Informed Trading Around Earnings Announcements: Another Look," The Financial Review, Eastern Finance Association, vol. 39(3), pages 409-434, August.
    18. Engle, Robert F. & Patton, Andrew J., 2004. "Impacts of trades in an error-correction model of quote prices," Journal of Financial Markets, Elsevier, vol. 7(1), pages 1-25, January.
    19. Sylwia Nowak, 2008. "How Do Public Announcements Affect The Frequency Of Trading In U.S. Airline Stocks?," CAMA Working Papers 2008-38, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    20. Rzayev, Khaladdin & Ibikunle, Gbenga, 2019. "A state-space modeling of the information content of trading volume," Journal of Financial Markets, Elsevier, vol. 46(C).
    21. Ben Sita, Bernard, 2010. "Autocorrelation of the trade process: Evidence from the Helsinki Stock Exchange," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 538-547, November.

    More about this item

    Keywords

    Arrival rates; informed trades; uninformed trades; autoregressive process; market depth; liquidity; volatility.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:wpa:wuwpfi:0207017. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: EconWPA The email address of this maintainer does not seem to be valid anymore. Please ask EconWPA to update the entry or send us the correct address (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    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.