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Algorithmic trading and forced CEO turnover: a learning hypothesis
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DOI：10.1007/s11142-026-09980-1.png)
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Abstract
En 中文
We examine the effect of algorithmic trading on forced CEO turnover and how boards respond to it. We find that the sensitivity of forced CEO turnover to stock returns declines with algorithmic trading, suggesting that algorithmic trading weakens directors’ learning from market prices. Boards respond to this information loss by placing greater weight on nonmarket-based performance measures, such as accounting performance and analyst expectations, and by meeting more frequently to gather information. Despite these efforts, boards make worse CEO turnover decisions when algorithmic trading is higher. Overall, our findings suggest that, while directors try to compensate for the reduction in price informativeness caused by algorithmic trading, their adjustments fail to fully offset its negative impact on board effectiveness.
Keywords:
Algorithmic trading
Stock returns
CEO turnover
Directors
Learning
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