Algorithmic Trading Regulation and Stock Market Liquidity: Evidence from China’s A-Share Market

Abstract

We examine how restricting high-frequency trading (HFT) affects stock liquidity in China’s A-share market. Using China’s 2024 Provisions on Program Trading in the Securities Market (Trial) as a quasi-natural experiment, we construct a stock-level high-frequency trading intensity index from tick-level order data and apply a difference-in-differences design. We find that stocks with greater pre-policy HFT exposure experience significantly larger reductions in quoted and effective spreads following the regulatory announcement. First-stage tests further show that several HFT-related trading behaviors decline more strongly among high-exposure stocks, providing behavioral support for the treatment measure. The liquidity effect is state-dependent, with significantly larger improvements following negative-return periods. Cross-sectional analyses further show stronger effects for stocks with higher pre-policy volatility and for margin-tradable stocks. These findings suggest that the liquidity benefits of algorithmic trading regulation are particularly pronounced when and where liquidity is more fragile, providing new evidence on the role of HFT regulation in retail-dominated emerging markets.