Measuring abnormal daily trading volume for samples of NYSE/ASE and NASDAQ securities using parametric and nonparametric test statistics

  • Cynthia J. Campbell
  • Charles E. Wasley


We extend prior research on the empirical properties of daily trading volume and methods to detect abnormal trading volume in two ways. We compare the performance of a nonparametric test statistic with the parametric test statistic used in prior research and we study samples of NASDAQ securities as well as samples of NYSE/ASE securities. Prior research has focused exclusively on NYSE securities. We find the nonparametric test statistic is more powerful in detecting abnormal trading volume than the parametric test statistic in both samples of NYSE/ASE and NASDAQ securities. We also document that abnormal trading volume will be detected more often in samples of NYSE/ASE securities compared to NASDAQ securities.

Key words

parametric/nonparametric test statistics daily trading volume abnormal trading volume event studies 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ajinkya, B.P. and P.C.Jain, “The Behavior of Daily Stock Market Trading Volume.” Journal of Accounting and Economics 11, 331–360, (1989).Google Scholar
  2. Brown, S. and J.Warner, “Using Daily Stock Returns: The Case of Event Studies.” Journal of Financial Economics 14, 3–31, (1985).Google Scholar
  3. Campbell, C.J. and C.E.Wasley, “Measuring Security Price Performance Using Daily NASDAQ Returns.” Journal of Financial Economics 33, 73–92, (1993).Google Scholar
  4. Corrado, C.J., “A Nonparametric Test for Abnormal Security-Price Performance in Event Studies.” Journal of Financial Economics 23, 385–395, (1989).Google Scholar
  5. Cready, W.M. and R.Ramanan, “The Power of Tests Employing Log-Transformed Trading Volume in Detecting Abnormal Trading.” Journal of Accounting and Economics 14, 203–215, (1991).Google Scholar
  6. Demski, J.S. and G.A.Feltham, “Market Response to Financial Reports.” Journal of Accounting and Economics 17, 3–40, (1994).Google Scholar
  7. Hettmansperger, T., Statistical Inference Based on Ranks. New York: John Wiley & Sons, 1984.Google Scholar
  8. Holthausen, R.W. and R.E.Verrecchia, “The Effects of Informedness and Consensus on Price and Volume Behavior. The Accounting Review 65, 191–208, (1990).Google Scholar
  9. Jain, P.C., “Analyses of the Distribution of Security Market Model Prediction Errors for Daily Returns Data.” Journal of Accounting Research 24, 76–96, (1986).Google Scholar
  10. Judge, G., W.Griffiths, R.Hill, H.Lutkepohl, and T.Lee, The Theory and Practice of Econometrics, 2nd ed. New York: Wiley, 1985.Google Scholar
  11. Kim, O. and R.E.Verrecchia, “Market Reaction to Anticipated Announcements.” Journal of Financial Economics 30, 273–309, (1991a).Google Scholar
  12. Kim, O. and R.E.Verrecchia, “Trading Volume and Price Reactions to Public Announcements.” Journal of Accounting Research 29, 302–321, (1991b).Google Scholar
  13. Kim, O. and R.E.Verrecchia, “Market Liquidity and Volume around Earnings Announcements.” Journal of Accounting and Economics 17, 41–67, (1994).Google Scholar
  14. Lehmann, E. Nonparametrics: Statistical Methods Based on Ranks. Oakland: Holden-Day, 1975.Google Scholar
  15. Stuart, A. and J.K.Ord, Kendall's Advanced Theory of Statistics. New York: Oxford University Press, 1987.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Cynthia J. Campbell
    • 1
    • 2
  • Charles E. Wasley
    • 3
  1. 1.U.S. Securities and Exchange CommissionWashington, DCUSA
  2. 2.School of ManagementUniversity of Massachusetts at AmherstAmherstUSA
  3. 3.Olin School of BusinessWashington UniversitySt. LouisUSA

Personalised recommendations