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

As algorithmic trading (AT) has become a dominant component in financial markets, it is important to understand its benefits and costs. We argue that AT enhances managers’ bad news hoarding by reducing monitoring from fundamental investors and exacerbating price reactions to bad news, thereby increasing stock price 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.