This study addresses the dual structural characteristics of China’s financial market—namely, “retail-investor dominance (80% of trading volume) versus foreign capital’s technological monopoly (0.3% of institutions controlling 43.6% of order flow).” By constructing a multi-layer complex network agent-based model (ABM) that integrates regulatory, core institutional, market-maker, and retail investor layers, it systematically simulates risk transmission mechanisms and regulatory strategies in high-frequency trading (HFT) environments. The findings reveal that HFT exacerbates market unfairness through technological latency advantages. When communication latency differentials exceed 50 milliseconds, retail order interception rates increase nonlinearly to 82%. Moreover, as the strategy homogenization coefficient ρ surpasses the critical threshold of 0.65, the market undergoes a percolation phase transition, with systemic risk probability jumping from 0.2 to over 0.7, which may trigger liquidity crises such as “flash crashes.” Traditional regulatory approaches, hindered by response delays averaging 2.1 h, struggle to cope with the real-time nature of HFT and the challenges posed by algorithmic black boxes. Based on the simulation results, policy recommendations centered on “anti-technological-monopoly,” “real-time algorithmic resonance monitoring,” and “regulatory intelligence” are proposed to develop a modernized and computationally executable regulatory framework tailored to China’s market structure, thereby enhancing both market stability and fairness.
Jian et al. (Thu,) studied this question.