From Nov 2023 to Oct 2026, 23 archived papers mentioned NYSE TAQ in their title, abstract, keywords or full text, most of them in Mar 2026 (3).
NYSE TAQ was mentioned in 1 paper in the last 30 days (2026-10-07 to 2026-11-05), ranking #165 of the 246 datasets mentioned.
By journal category: Economics (1).
Most of them appeared in Computational Economics (1).
From 2017 to 2026 (so far), NYSE TAQ was mentioned in 27 papers in the tracked journals, most of them in finance (12), accounting (8) and economics (5).
6 of them were published in 2025, against 0 in 2017.
Since 2023, the institutions with the most of these papers were London School of Economics and Political Science (3), Systemic Risk Centre (3) and Koç University (2).
The journals publishing most of them were Review of Accounting Studies (7), International Review of Financial Analysis (3) and Journal of Financial Economics (2).
The most-cited of them is “Applications of high-frequency data in finance: A bibliometric literature review” (International Review of Financial Analysis, 2023), with 37 citations.
Papers use NYSE TAQ most often with CRSP (14), Compustat (11) and I/B/E/S (10); it has 15 partners in the code and data network, in the group around S&P 500, Economic Policy…, Compustat.
Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.
Papers use NYSE TAQ most often with CRSP (14), Compustat (11) and I/B/E/S (10); it has 15 partners in the code and data network, in the group around S&P 500, Economic Policy…, Compustat. The whole network →
What papers use NYSE TAQ together with (at least 3 papers; lift 1 = chance)
Scholar4AI. (2026). NYSE TAQ. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/nyse-taq
@misc{scholar4ai-datasets-nyse-taq,
author = {{Scholar4AI}},
title = {NYSE TAQ},
year = {2026},
howpublished = {\url{https://scholar4ai.com/datasets/nyse-taq}},
urldate = {2026-11-05},
note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).