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This study aims to integrate meta-analysis into agent-based models to provide foundational insights into biased agent interactions. It delves into how various behavioral biases, such as anchoring, disposition effect, loss aversion, and others, influence market dynamics and investor decisions. By utilizing agent-based models, it offers simulations of market scenarios and investor behaviors, highlighting the impact of individual decisions on market dynamics. The innovative approach of this study lies in its integration of behavioral finance theories with real market data, offering a nuanced analysis of market behaviors. This work contributes a new perspective to behavioral finance and encourages the use of agent-based models to deepen the understanding of market dynamics and investor behaviors, potentially aiding in financial market analysis and policy formulation. This study aims to provide a foundational infrastructure for research that wishes to integrate meta-analysis into agent-based models and enable the examination of biased agent behaviors. The findings of this study have managed to model the interactions of loss aversion, disposition effect, and anchoring and adjustment bias in the closest manner to the real world by considering agents' socio-demographic and psychological factors. The results are highly conducive to more accurately modeling human behaviors in portfolio optimizations and expanding the applications of Generalized Artificial Intelligence in financial markets.
Ömür Saltık (Fri,) studied this question.