Algorithmic Trading and Its Impact on Financial Markets: The Norwegian Government Pension Fund Global: Case Study

The connection between the acceptance of algorithmic trading and financial market performance is investigated in this paper using the Norwegian Government Pension Fund Global (GPFG) as a case study. Using a Panel Autoregressive Distributed Lag (ARDL) model, the paper examines quarterly data from 2010–2024 encompassing 15 developed markets where the GPFG conducts (N = 900 observations). The paper examines how algorithmic trading intensity influences measures of market liquidity, volatility, and efficiency. Using Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed impacts (DFE) estimators, the study indicates mixed impacts on volatility but a noteworthy long-term positive correlation between algorithmic trading adoption and market liquidity (coefficient: 0.247, p < 0.01). The error correcting mechanism indicates a quarterly adjustment of 23.4% toward long-run equilibrium. While regulatory systems have to deal with possible systemic hazards, the results suggest that using methodical algorithmic tactics will help big institutional investors, including sovereign wealth funds, increase market efficiency. These results have major consequences for financial market control and institutional investment policy.