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A Trading Volume Benchmark: Theory and Evidence

Open access
Tkac, Paula A.·1999·The Journal of Financial and Quantitative Analysis

Abstract

This paper provides a theoretical rebalancing benchmark for trading volume that delivers a connection between trading activity in individual stocks and market-wide volume. This model supports the empirical use of an adjustment for market-wide trading activity when filtering out normal trading volume. Data on a sample of large NYSE/AMEX firms support the usefulness of the benchmark. While 20% of the sample firms exhibit trading behavior that is consistent with the cross-sectional prediction of the rebalancing benchmark, systematic deviations exist. An analysis of deviations from the benchmark allows a characterization of anomalous trading activity. I find that average excess turnover vs. the benchmark is positively related to option availability and institutional ownership and negatively related to firm size. The data do not yield a uniform conclusion on the effect of S&P 500 inclusion. S&P 500 inclusion does not significantly increase the trading of firms that are already trading above benchmark levels, but does result in additional trading for firms that undertrade the benchmark prior to inclusion. An investigation of individual firm market model regressions indicates that this is a useful methodology for filtering out the anomalous trading documented here.

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Metadata

Authors
Tkac, P. A.
Venue
The Journal of Financial and Quantitative Analysis
Publisher
JSTOR
Published
1999-03
Volume
34
Issue
1
Pages
89
ISSN
0022-1090 (ISSN-L)
OpenAlex
W2130917432
Cited by
178
References
1
FWCI
21.88
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A Trading Volume Benchmark: Theory and Evidence. https://app.cochat.ai/papers/10.2307/2676247
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Tkac, P. A. (1999). A Trading Volume Benchmark: Theory and Evidence. The Journal of Financial and Quantitative Analysis. https://doi.org/10.2307/2676247

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.