Work

The Rise of Machines: Algorithmic Trading and Stock Price Crash Risk

Year: 2025

Type: article

Abstract: As algorithmic trading (AT) has become a dominant component in financial markets, it is important to understand its benefits and costs. Theory predicts that traders with a short horizon overweigh short-term information relative to firm fundamentals. We argue that because algorithmic traders typically have very short horizons, AT likely motivates managers to focus on short-term earnings and hoard bad news, thus leading to higher crash risk. Consistent with our prediction, we find that AT is positively related to future stock price crash risk. We also find that AT is associated with managers' opportunistic financial reporting and disclosure decisions and that the effect of AT on crash risk is more pronounced when managers have greater ability or incentives to withhold bad news. We further validate our findings using an instrumental variable analysis and the SEC's 2016 Tick Size pilot program as a quasi-natural experiment. Overall, our results suggest AT is positively related to firm-specific stock price crash risk, which could potentially have devastating effects on shareholder welfare.

Source: Journal of Accounting Auditing & Finance

DOI: 10.1177/0148558x251394477

Authors: Anwer S. Ahmed, Yiwen Li, Andrew McMartin, Nina Xu

Institutions: Texas A&M University, Villanova University, University of Delaware, University of Connecticut

Language: English

Topic: Auditing, Earnings Management, Governance

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W4416971542

FWCI:

Cites: 78

Cited by: 2

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