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Algorithmic trading and post-earnings-announcement drift: A cross-country study
Tao Chen
2023-03-01
Source PublicationInternational Journal of Accounting
ABS Journal Level3
ISSN0020-7063
Volume58Issue:1Pages:2350003
Abstract

Synopsis The research problem This study investigates whether algorithmic trading matters to post-earnings-announcement drift (PEAD) across 41 countries. Motivation The 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 hypotheses H1: 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 methodology Ordinary Least Square (OLS) Regressions. Analyses We 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. Findings We 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.

KeywordAlgorithmic Trading Disclosure Requirements Information Dissemination Investor Protection Post-earnings-announcement Drift
DOI10.1142/S1094406023500038
URLView the original
Indexed ByESCI
Language英語English
WOS Research AreaBusiness & Economics
WOS SubjectBusiness, Finance
WOS IDWOS:000918685600003
Scopus ID2-s2.0-85147129090
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF FINANCE AND BUSINESS ECONOMICS
Corresponding AuthorTao Chen
AffiliationDepartment of Finance and Business Economics,Faculty of Business Administration,University of Macau,Taipa,Macao
First Author AffilicationFaculty of Business Administration
Corresponding Author AffilicationFaculty of Business Administration
Recommended Citation
GB/T 7714
Tao Chen. Algorithmic trading and post-earnings-announcement drift: A cross-country study[J]. International Journal of Accounting, 2023, 58(1), 2350003.
APA Tao Chen.(2023). Algorithmic trading and post-earnings-announcement drift: A cross-country study. International Journal of Accounting, 58(1), 2350003.
MLA Tao Chen."Algorithmic trading and post-earnings-announcement drift: A cross-country study".International Journal of Accounting 58.1(2023):2350003.
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