The DOI matches its Crossref record.
One Day in the Life of a Very Common Stock
Abstract
Using the model structure of Easley and O'Hara (Journal of Finance, 47, 577–604), we demonstrate how the parameters of the market-maker's beliefs can be estimated from trade data. We show how to extract information from both trade and no-trade intervals, and how intraday and interday data provide information. We derive and evaluate tests of model specification and estimate the information content of differential trade sizes. Our work provides a framework for testing extant microstructure models, shows how to extract the information contained in the trading process, and demonstrates the empirical importance of symmetric information models for asset prices.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Does Stock Liquidity Enhance or Impede Firm Innovation?
FANG, V. W., TIAN, X., & TICE, S. (2014) · The Journal of Finance · doi:10.1111/jofi.12187 · Cited by 1,339
Option Volume and Stock Prices: Evidence on Where Informed Traders Trade
Easley, D., O'Hara, M., & Srinivas, P. S. (1998) · The Journal of Finance · doi:10.1111/0022-1082.194060 · Cited by 1,084
Self-citationAmihud, Y., Mendelson, H., & Pedersen, L. H. (2005) · Foundations and Trends® in Finance · doi:10.1561/0500000003 · Cited by 820
Conference calls and information asymmetry
Brown, S., Hillegeist, S. A., & Lo, K. (2004) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2004.02.001 · Cited by 482
Cross-Listing, Investment Sensitivity to Stock Price, and the Learning Hypothesis
Foucault, T., & Frésard, L. (2012) · Review of Financial Studies · doi:10.1093/rfs/hhs093 · Cited by 336
Metadata
- Authors
- Easley, D., Kiefer, N. M., & O'Hara, M.
- Venue
- Review of Financial Studies
- Publisher
- Oxford University Press (OUP)
- Published
- 1997-07
- Volume
- 10
- Issue
- 3
- Pages
- 805-835
- ISSN
- 0893-9454 (ISSN-L)
- 1465-7368
- OpenAlex
- W1988135531
- Cited by
- 634
- References
- 0
- FWCI
- 23.41
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: One Day in the Life of a Very Common Stock. https://app.cochat.ai/papers/10.1093/rfs/10.3.805
- The paper
- Easley, D., Kiefer, N. M., & O'Hara, M. (1997). One Day in the Life of a Very Common Stock. Review of Financial Studies. https://doi.org/10.1093/rfs/10.3.805
Data sources
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.