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Status即將出版Forthcoming
Algorithmic trading and post-earnings-announcement drift: A cross-country study
Tao Chen
2022-12
Source PublicationInternational Journal of Accounting
ABS Journal Level3
Other Abstract

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

Hypothesis 1: Earnings-announcement algorithmic trading does not impact PEAD.

 

Hypothesis 2: Country-level investor protection does not impact the association between earnings-announcement algorithmic trading and PEAD.

 

Hypothesis 3: Country-level information dissemination does not impact the association between earnings-announcement algorithmic trading and PEAD.

 

Hypothesis 4: 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 Squares (OLS) Regressions.

 

Analyses

We follow Saglam (2020) 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 Post-earnings-announcement Drift Investor Protection Information Dissemination Disclosure Requirements
Indexed ByESCI
PublisherWorld Scientific
Fulltext Access
Document TypeJournal article
CollectionFaculty of Business Administration
DEPARTMENT OF FINANCE AND BUSINESS ECONOMICS
Corresponding AuthorTao Chen
AffiliationUniversity of Macau
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Tao Chen. Algorithmic trading and post-earnings-announcement drift: A cross-country study[J]. International Journal of Accounting,2022.
APA Tao Chen.(2022).Algorithmic trading and post-earnings-announcement drift: A cross-country study.International Journal of Accounting.
MLA Tao Chen."Algorithmic trading and post-earnings-announcement drift: A cross-country study".International Journal of Accounting (2022).
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