Advanced simulation paradigm of human behaviour unveils complex financial systemic projection
This addresses the problem of financial market projection for analysts and policymakers by simulating investor behavior, though it appears incremental as it builds on agent-based modeling.
The paper tackles the challenge of projecting financial markets by simulating human behavior, proposing a new behavioral simulation paradigm with hierarchical knowledge architecture for agents. The simulator achieves 13.29% deviation in crisis scenarios with 285.34% price increases and lower mean square error in normal conditions for futures markets.
The high-order complexity of human behaviour is likely the root cause of extreme difficulty in financial market projections. We consider that behavioural simulation can unveil systemic dynamics to support analysis. Simulating diverse human groups must account for the behavioural heterogeneity, especially in finance. To address the fidelity of simulated agents, on the basis of agent-based modeling, we propose a new paradigm of behavioural simulation where each agent is supported and driven by a hierarchical knowledge architecture. This architecture, integrating language and professional models, imitates behavioural processes in specific scenarios. Evaluated on futures markets, our simulator achieves a 13.29% deviation in simulating crisis scenarios whose price increase rate reaches 285.34%. Under normal conditions, our simulator also exhibits lower mean square error in predicting futures price of specific commodities. This technique bridges non-quantitative information with diverse market behaviour, offering a promising platform to simulate investor behaviour and its impact on market dynamics.