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Herding, momentum and investor over-reaction

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Abstract

In this paper we study the impact of noise or quality of prices on returns. The noise arises from herding by market participants beyond what is justified by information. We construct a firm-quarter-specific measure of speculative intensity (SPEC) based on autocorrelation in daily trading volume adjusted for the amount of information available, and find that speculative intensity has a significant positive impact on returns. Both cross-sectional and time series variation in SPEC are consistent with conventional wisdom, and with implications of theories of herding as in DeLong et al. (1990, J Political Econ 98(4):703–738). We find that high-SPEC firms drive the returns to momentum trading strategies and that investor over-reaction is significant only in the case of high-SPEC firms.

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Notes

  1. see, e.g., DeLong et al. (1990), Dow and Gorton (1994), Grinblatt et al. (1995) Sias (2004).

  2. These information proxies are general proxies and do not distinguish between private and public information.

  3. Hereafter we refer to ‘noise in prices’ and ‘speculative intensity’ interchangeably.

  4. See, e.g., Sharma et al. (2004) for a more detailed analysis.

  5. The 5 days of the week dummies serve as the intercept term, inspired by previous work (e.g., Brusa and Liu 2004).

  6. The primary goal in Ajinkya and Jain (1989) is not to identify the time series properties of individual firms’ daily trading volumes but to refine the market model for volume used in studies of volume reaction to earnings announcements.

  7. We also investigated the autocorrelation properties of daily trading volume in the firm-quarters that had at least 25 observations. In an overwhelming preponderance of cases the first lag was the most important.

  8. Our results are robust to limiting our sample to autocorrelations that are calculated using 50 or more trading days.

  9. The actual implementation used the MIXED procedure in SAS.

  10. Our sample ends with the 3rd quarter of 2004.

  11. We also used total assets, and it made little difference.

  12. We report results using VARRET defined as the variance of the daily returns adjusted for the value weighted market return calculated for each company quarter.

  13. We also ran all of our regressions without VARRET as a control variable, and it turns out the same qualitative conclusions continue to hold. But because a priori it is likely the LHS variance is not constant in a sample spanning 20 years we choose to report the results with VARRET.

  14. For instance the correlation between the ACCs generated by the firm volumes not adjusted for market volume and the market adjusted volume is 0.78 with p-value of less then 0.0001.

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Acknowledgments

We have benefited from comments and suggestions by seminar participants at CUNY-Baruch, Georgetown, Maryland, Rutgers, Waterloo, the Conference on Financial Economics and Accounting, and the Washington Area Finance Association Conference. The paper benefited substantially from help received from Srini Sankaraguruswamy. We also thank Bruce Lehmann, Bikki Jaggi, Prem Jain, Russell Lundholm, Sandeep Patel, Sundaresh Ramnath and Rohan Williamson. Special thanks are due to our discussants at the FEA Conference, Sudhakar Balachandran, and the WAFA Conference, Chris Jones, for very detailed comments.

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Authors and Affiliations

  1. Department of Accountancy, Bentley College, 175 Forest Street, Waltham, MA, 02452-4705, USA

    Rani Hoitash

  2. Department of Accounting, Yeshiva University, 500 West 185th Street, New York, NY, 10033, USA

    Murugappa (Murgie) Krishnan

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  1. Rani Hoitash
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  2. Murugappa (Murgie) Krishnan
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Correspondence to Murugappa (Murgie) Krishnan.

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Data Availability: The data used in this study is available from public sources cited in the text.

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Hoitash, R., Krishnan, M. Herding, momentum and investor over-reaction. Rev Quant Finan Acc 30, 25–47 (2008). https://doi.org/10.1007/s11156-007-0042-y

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