Trading Volume and Cross-Autocorrelations in Stock Returns


  • Tarun Chordia,

  • Bhaskaran Swaminathan

  • Chordia is from Vanderbilt University and Swaminathan is from Cornell University. We thank Clifford Ball, Doug Foster, Roger Huang, Charles Lee, Craig Lewis, Ron Masulis, Matt Spiegel, Hans Stoll, Avanidhar Subrahmanyam, two anonymous referees, the editor René Stulz, and seminar participants at the American Finance Association meetings, Eastern Finance Association meetings, Southern Finance Association meetings, Southwestern Finance Association meetings, Utah Winter Finance Conference, Chicago Quantitative Alliance, and Vanderbilt University for helpful comments. We are especially indebted to Michael Brennan for stimulating our interest in this area of research. The first author acknowledges support from the Dean's Fund for Research and the Financial Markets Research Center at Vanderbilt University. The authors gratefully acknowledge the contribution of I/B/E/S International Inc. for providing analyst data. All errors are solely ours.


This paper finds that trading volume is a significant determinant of the lead-lag patterns observed in stock returns. Daily and weekly returns on high volume portfolios lead returns on low volume portfolios, controlling for firm size. Nonsynchronous trading or low volume portfolio autocorrelations cannot explain these findings. These patterns arise because returns on low volume portfolios respond more slowly to information in market returns. The speed of adjustment of individual stocks confirms these findings. Overall, the results indicate that differential speed of adjustment to information is a significant source of the cross-autocorrelation patterns in short-horizon stock returns.