How Does Algorithmic Trading Improve Market Quality?

We use a comprehensive panel of NYSE limit order book data to investigate the channel by which algorithmic trading (AT) improves market quality. We find that enhanced market maker monitoring explains the majority of improvements in liquidity and quoting efficiency during the 2000s. Market maker monitoring subsumes the ratio of order cancellations to total volume (a broad measure of AT) in accounting for improvements in market quality. Moreover, the residual variation in AT not associated with our AT market making proxy is typically associated with higher spreads, suggesting that different categories of algorithmic traders have distinct effects on market function. To distinguish decreased monitoring costs from potential confounds, we develop a stylized model of constrained market maker attention and empirically verify unique predictions concerning market maker behaviors around idiosyncratic versus multi-asset price jumps and small versus large stock price jumps. Our results provide a novel explanation for why spreads have not continued to fall since 2007 despite sustained increases in AT.