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

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  • Tao Chen

    (Department of Finance and Business Economics, Faculty of Business Administration, University of Macau, Taipa, Macau)

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.

Suggested Citation

  • Tao Chen, 2023. "Algorithmic Trading and Post-Earnings-Announcement Drift: A Cross-Country Study," The International Journal of Accounting (TIJA), World Scientific Publishing Co. Pte. Ltd., vol. 58(01), pages 1-38, March.
    • Handle: RePEc:wsi:tijaxx:v:58:y:2023:i:01:n:s1094406023500038
    DOI: 10.1142/S1094406023500038
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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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