Algorithmic trading, liquidity and volatility: Evidence from Poland

The aim of this paper is to examine the causality between pairs of measures that describe the intensity of algorithmic trading, market liquidity and volatility for selected blue-chip companies from the Warsaw Stock Exchange, which were permanently included in the WIG20 index from January 1, 2020, to August 31, 2023. In the study, both daily and high-frequency intraday data are used. The research is based on fundamental concepts of information theory, namely entropy and transfer entropy. Additionally, Rényi entropy is used to examine the causal relationships between extreme values of the variables. Our results, based on Shannon’s transfer entropy, suggest that algorithmic trading affects liquidity and volatility. The main finding is that if the frequency increases, the number of companies for which information transfer is significant also grows. However, this relationship is not observed for extreme values, for which Rényi entropy is applied.