Does Algorithmic Trading Induce Herding?
研究了算法交易如何影响股票市场的羊群行为，发现其引发的羊群效应是非算法交易的14倍，且在国际波动和不确定性高时更显著，但在本地波动高时出现“忽视效应”。
ABSTRACT Algorithmic trading (AT) plays a major role in the trading activities of developed markets. This research breaks new ground by investigating how AT influences herding behaviour in stock markets. Utilising the implementation of the Markets in Financial Instruments Directive (MiFID II), we show that AT‐induced herding is quantitatively 14 times more pronounced compared to herding triggered by non‐AT elements. Algorithmic traders herd more when international volatility and market uncertainty are high, revealing a heightened sensitivity to global market signals. However, during periods of high local volatility, AT seems to disregard these fluctuations, indicating an ‘inattention effect’. AT‐induced anti‐herding is prominent in the volatile aggressive stocks, while no such behaviour is observed in the more stable defensive stocks. The findings carry critical implications for both regulators and market professionals, as we uncover dual behaviours of AT‐induced herding and anti‐herding in varying market conditions.