An Agent-Based Computational Finance Simulation Model to Study Market Efficiency

Abstract

The advancement of computational modeling, data systems, and digital infrastructure has enabled the rise of agent-based computational finance (ACF). This study models interactions among heterogeneous investors. By embedding behavioral logics such as environmental, social, and governance (ESG) preferences and volatility thresholds, the model captures microstructural dynamics under different trading rules. Using ACF, the authors compare transaction plus 0 day (T+0) to transaction plus 1 day (T+1). Results show that T+0 improves price discovery, deepens liquidity, and reduces transaction costs. From a computational perspective, this research contributes to ACF by showing how policy logic and investor heterogeneity can be encoded and tested in a replicable simulation environment. The simulation method offers a scalable approach for regulators to assess sustainability-aligned reforms under diverse institutional settings. The study bridges computational finance and digital governance, highlighting the potential of integrating advanced IT into ACF to support adaptive, climate-conscious market design.