Algorithmic trading and market efficiency around the introduction of the NYSE Hybrid Market
研究了算法交易对市场效率的影响，发现纽约证券交易所混合市场引入后算法交易的外生增加显著降低了市场订单不平衡和限价订单簿不平衡的预测能力，表明算法交易通过促进订单流中信息的融入提高了市场效率。
I study the effect of algorithmic trading on market efficiency, taking into account past market and limit order flows alike. I find that an exogenous increase in algorithmic trading around the introduction of the NYSE Hybrid Market leads to a significant decrease in the predictive power of surprises in market order imbalance and limit order book imbalances, especially at the outer levels of the limit order book. However, the predictive power of past returns remains largely unchanged. This suggests that algorithmic trading improves market efficiency by facilitating the incorporation of information embedded in both market and limit order flows.