How Does Algorithmic Trading Improve Market Quality?

Abstract

We use a comprehensive panel of NYSE order book data to show that the liquidity and quoting efficiency improvements associated with algorithmic trading (AT) are attributable to enhanced monitoring by liquidity providers. We find that variation in liquidity provider monitoring uniquely explains quoting behaviors around idiosyncratic versus multi-asset price jumps and small- versus large-stock price jumps. In addition, we find monitoring outperforms measures of overall AT activity in explaining stock-level decreases in liquidity costs, and that residual variation in AT is associated with increased spreads. Importantly, our results indicate that there are diminishing returns to market function from subsequent technological advancements, thus providing a novel explanation for why spreads have not continued to fall since 2007 despite sustained increases in algorithmic trading.

Keywords: High-Frequency Market Making, Algorithmic Trading, Adverse Selection, Monitoring Costs

JEL Classification: G10, G12, G14