Algorithmic Trading in A-share and Stock Price Informativeness before Earnings Announcements
Algorithmic trading has emerged in the A-share market in recent years,and its impact on capital market pricing efficiency has received wide attention across industry and academia.This paper focus on companies listed on the SZE Growth Enterprises Market and SSE STAR Market,aiming to explore the influence of algo-rithmic trading on the information content of stock prices before the release of quar-terly earnings announcements.Firstly,by considering the trading system features and investor structure characteristics of the A-share market,this paper constructs algorithmic trading indicators tailored to the A-share market.Based on these indica-tors,empirical tests reveal that algorithmic trading reduces the information content of stock prices before earnings announcements,indicating that the liquidity demand strategy of algorithmic trading plays a dominant role.Further mechanism analysis shows that the negative impact of algorithmic trading on stock price information content stems from increased transaction costs for slower investors and reduced large orders from informed traders.Lastly,the research finds that while algorithmic trading exhibits a crowding-out effect on informed traders,it also mitigates abnormal stock price fluctuations caused by noise trading.This study deepens our understanding of the economic consequences of algorithmic trading at the market level and provides insights for regulators to improve related policies concerning algorithmic trading.
algorithmic tradingearnings announcementinformational content of stock price
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