Does Algorithmic Trading Affect Forced CEO Turnover?

Abstract

We examine whether algorithmic trading (AT) affects the extent to which directors rely on stock returns when making CEO turnover decisions. We find that the sensitivity of forced CEO turnover to stock returns decreases with AT. This effect of AT is more pronounced when the information crowded out by AT is likely to be useful to directors, when there is more informed trading that AT dampens, and when the directors’ own information set is poor. In contrast, the effect of AT does not vary with the strength of firms’ corporate governance. Our results hold under an instrumental variable approach and a randomized controlled experiment conducted by the U.S. Securities and Exchange Commission’s Tick Size Pilot Program. Overall, our findings suggest that directors incorporate information from stock returns into their CEO turnover decisions and that AT hinders such learning.

Keywords: Algorithmic trading, stock returns, CEO turnover, directors, learning, market feedback

JEL Classification: G30, J63, L20, E44, G19