The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
What Are the Best Liquidity Proxies for Global Research?
Open accessAbstract
Liquidity plays an important role in global research. We identify high-quality liquidity proxies based on low-frequency (daily) data, which provide 1,000× to 10,000× computational savings compared to computing high-frequency (intraday) liquidity measures. We find that: (i) Closing Percent Quoted Spread is the best monthly percent-cost proxy when available, (ii) Amihud, Closing Percent Quoted Spread Impact, LOT Mixed Impact, High–Low Impact, and FHT Impact are tied as the best monthly cost-per-dollar-volume proxy, (iii) the daily version of Closing Percent Quoted Spread is the best daily percent-cost proxy, and (iv) the daily version of Amihud is the best daily cost-per-dollar-volume proxy.
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
Cites
Just how much do individual investors lose by trading?
Barber, B. M., Lee, Y.-T., Liu, Y.-J., & Odean, T. (2009) · Review of Financial Studies · doi:10.1093/rfs/hhn046 · Cited by 953
Transparency, Liquidity, and Valuation: International Evidence on When Transparency Matters Most
LANG, M., LINS, K. V., & MAFFETT, M. (2012) · Journal of Accounting Research · doi:10.1111/j.1475-679x.2012.00442.x · Cited by 821
Cited by
The effect of pro-environmental preferences on bond prices: Evidence from green bonds
Zerbib, O. D. (2019) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2018.10.012 · Cited by 1,379
Empirical Asset Pricing via Machine Learning: The Role of Research Design Choices
Lalwani, V., Meshram, V., & Jindal, V. (2026) · European Financial Management · doi:10.1111/eufm.70033
Metadata
- Authors
- Fong, K. Y. L., Holden, C. W., & Trzcinka, C. A.
- Venue
- Review of Finance
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-03-13
- Volume
- 21
- Issue
- 4
- Pages
- 1355-1401
- ISSN
- 1382-6662 (ISSN-L)
- 1572-3097
- 1573-692X
- OpenAlex
- W3121425277
- Cited by
- 502
- References
- 2
- FWCI
- 18.39
- Type
- journal article
- Language
- en
- Open access
- green
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: What Are the Best Liquidity Proxies for Global Research? https://app.cochat.ai/papers/10.1093/rof/rfx003
- The paper
- Fong, K. Y. L., Holden, C. W., & Trzcinka, C. A. (2017). What Are the Best Liquidity Proxies for Global Research? Review of Finance. https://doi.org/10.1093/rof/rfx003
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.