Attention: How high-frequency trading improves price efficiency following earnings announcements

Bidisha Chakrabarty, Pamela C. Moulton and Wang, Xu (Frank)

Journal of Financial Markets, 2022, vol. 57, issue C

Abstract: Recent research indicates that high-frequency trading (HFT) helps incorporate fundamental information into prices, but how this happens is unclear. We examine reduced attention constraints as an important channel through which HFT enhances price efficiency. Using multiple proxies of attention constraints, we find that price inefficiencies are reduced by 65%–100% when high-frequency traders (HFTs) trade following low attention earnings announcements: initial price responses are larger and post-earnings-announcement drift is reduced. Results are not driven by firm size or announcement time-of-day. Our findings highlight how limited attention, a human bias affecting asset prices, is mitigated when machines trade.

Keywords: High-frequency trading; Limited attention; Price efficiency; Earnings announcements (search for similar items in EconPapers)
JEL-codes: G02 G10 G14 M40 M41 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S138641812100063X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finmar:v:57:y:2022:i:c:s138641812100063x

DOI: 10.1016/j.finmar.2021.100690

Access Statistics for this article

Journal of Financial Markets is currently edited by B. Lehmann, D. Seppi and A. Subrahmanyam

More articles in Journal of Financial Markets from Elsevier
Bibliographic data for series maintained by Catherine Liu ().