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Although predictive AI models have grown to dominate computational finance, they are often limited in their applications when it comes to studying interventions and explaining behavioral outcomes. Financial economics, on the other hand, has a rich history of analytical approaches to asset-pricing theory, often requiring sweeping assumptions. In this paper, we construct an agent-based model of asset markets that is able to dispense with onerous restrictions on agent behaviors and beliefs, while having analytical validity and providing insights into the functioning of asset markets. In particular, we evaluate our models with respect to several traditional financial economic theories like Tobin’s separation theorem and the capital asset pricing model (CAPM). We devise a network representing trades to show the emergence of different roles played by the agents. We study interventions, such as shocks, and explain the outcomes using our model. Finally, we investigate the effects of noise trading and show that noisy agents converge to different equilibrium points due to their differences in beliefs. Put together, this paper presents an agent-based model that can be used to study the effects of heterogeneous beliefs and risks of the agents and shocks to assets at a systemic level, thereby connecting localized agent and asset characteristics to global or collective outcomes.