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Review of Quantitative Finance and Accounting
Erschienen in: Review of Quantitative Finance and Accounting 1/2008

01.01.2008 | Original Paper

Herding, momentum and investor over-reaction

verfasst von: Rani Hoitash, Murugappa (Murgie) Krishnan

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 1/2008

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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.
Vorheriger Artikel Change in value relevance of quarterly foreign sales data of U.S. multinational corporations after adopting SFAS 131
Nächster Artikel Option volume, strike distribution, and foreign exchange rate movements

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Fußnoten
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.
 
Literatur
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Metadaten
Titel
Herding, momentum and investor over-reaction
verfasst von
Rani Hoitash
Murugappa (Murgie) Krishnan
Publikationsdatum
01.01.2008
Verlag
Springer US
Erschienen in
Review of Quantitative Finance and Accounting / Ausgabe 1/2008
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI
https://doi.org/10.1007/s11156-007-0042-y

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