Chordia_lead_lag - THE JOURNAL OF FINANCE VOL LV NO 2 APRIL...

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Trading Volume and Cross-Autocorrelations in Stock Returns TARUN CHORDIA and BHASKARAN SWAMINATHAN* ABSTRACT 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 port- folios lead returns on low volume portfolios, controlling for firm size. Nonsynchro- nous 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 pat- terns in short-horizon stock returns. B OTH ACADEMICS AND PRACTITIONERS HAVE LONG BEEN interested in the role played by trading volume in predicting future stock returns. 1 In this paper, we ex- amine the interaction between trading volume and the predictability of short horizon stock returns, specifically that due to lead-lag cross-autocorrelations in stock returns. Our investigation indicates that trading volume is a sig- nificant determinant of the cross-autocorrelation patterns in stock returns. 2 We find that daily or weekly returns of stocks with high trading volume lead daily or weekly returns of stocks with low trading volume. Additional tests indicate that this effect is related to the tendency of high volume stocks to respond rapidly and low volume stocks to respond slowly to marketwide information. * 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 As- sociation meetings, Southern Finance Association meetings, Southwestern Finance Association meetings, Utah Winter Finance Conference, Chicago Quantitative Alliance, and Vanderbilt Uni- versity 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 0 B 0 E 0 S International Inc. for providing analyst data. All errors are solely ours. 1 For the literature on volume and volatility see Karpoff ~ 1987 ! and Gallant, Rossi, and Tauchen ~ 1992 ! . 2 To be specific, we use the average daily stock turnover as a proxy for trading volume. THE JOURNAL OF FINANCE • VOL. LV, NO. 2 • APRIL 2000 913
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This paper is closely related to the literature on cross-autocorrelations initiated by Lo and MacKinlay ~ 1990 ! . Lo and MacKinlay find that positive autocorrelations in portfolio returns are due to positive cross-autocorrelations among individual security returns. Specifically, they find that the correla-
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  • Spring '12
  • MichaelKearns
  • Null hypothesis, Autocorrelation, Statistical hypothesis testing, Modern portfolio theory, returns, Autocorrelations

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