The information battle between algorithmic trading and ESG fund investors

Using data from China’s A-share market and a welfare-based measure of price informativeness, we find that algorithmic trading (AT) reduces the price informativeness about future cash flows. It does so by eroding ESG investors’ information rents, reducing their incentives to produce information and revealing a conflict between AT and ESG fund investors. We find that the negative effect of AT on price informativeness is more pronounced in firms with higher ESG investor ownership. Further evidence shows that AT reduces ESG-related questions from investors on interactive platforms (primarily in the environmental dimension), lowers idiosyncratic volatility during pre-ESG rating announcement windows, and diminishes price informativeness about future carbon emissions, confirming that AT targets ESG investors. We develop a theoretical model to formalize the above mechanism. Finally, AT’s negative impact on price informativeness is stronger for growth firms, high-sentiment firms, and heavy polluters. This study is the first to link AT and ESG investing, guiding the reconciliation of technological progress with sustainable finance.