Algorithmic Trading and Post-Earnings-Announcement Drift: A Cross-Country Study

Tao Chen

The International Journal of Accounting (TIJA), 2023, vol. 58, issue 01, 1-38

Abstract: SynopsisThe research problemThis study investigates whether algorithmic trading matters to post-earnings-announcement drift (PEAD) across 41 countries.MotivationThe increasing importance of algorithms has sparked interest in how computer-triggered trades affect the formation of securities prices. Thus, a large body of research has emerged to probe the instantaneous impact of algorithmic trading on price discovery; however, little work explores the role of algorithms in efficient pricing of low-frequency financial statements. In addition, the literature on PEAD always highlights firm-level drivers of this phenomenon, whereas its country-level institutional determinants remain silent.The test hypothesesH1: Earnings-announcement algorithmic trading does not impact PEAD.H2: Country-level investor protection does not impact the association between earnings-announcement algorithmic trading and PEAD.H3: Country-level information dissemination does not impact the association between earnings-announcement algorithmic trading and PEAD.H4: Country-level disclosure requirements do not impact the association between earnings-announcement algorithmic trading and PEAD.Target population Various stakeholders include market traders, firm managers, regulators, and scholars.Adopted methodologyOrdinary Least Square (OLS) Regressions.AnalysesWe follow Saglam [(2020) Financial Management, 49, 33–67] to measure algorithmic trading using the transaction-level data. Based on a global sample covering 41 markets, we estimate the regression of PEAD on four proxies for algorithmic trading after considering firm-specific controls and fixed effects of country and year.FindingsWe find a negative and significant association between earnings-announcement algorithmic activity and PEAD. The documented relation retains despite addressing the endogeneity problem. Further analyses indicate that algorithmic participation mitigates investor disagreement, alleviates trader distraction, and reduces market friction, thus facilitating efficient pricing of earnings information. Finally, the impact of algorithmic trading on PEAD is more prominent in countries with stronger investor protection, faster information dissemination, and stricter disclosure requirements.

Keywords: Algorithmic trading; post-earnings-announcement drift; investor protection; information dissemination; disclosure requirements (search for similar items in EconPapers)
JEL-codes: G12 G14 M41 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S1094406023500038
Access to full text is restricted to subscribers

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:wsi:tijaxx:v:58:y:2023:i:01:n:s1094406023500038

Ordering information: This journal article can be ordered from

DOI: 10.1142/S1094406023500038

Access Statistics for this article

The International Journal of Accounting (TIJA) is currently edited by A. Rashad Abdel-khalik

More articles in The International Journal of Accounting (TIJA) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().