Year of Graduation
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Restricted Access Thesis
Department or Program
Artificial neural networks and other predictive models have grown to dominate computational finance. One recent application of such networks is the generation of synthetic market data for machine learning models. Unfortunately, such generative models has limited application when it comes to studying interventions and explaining behavioral outcomes. In this thesis, I study agent-based modeling -- an established technique with a significant history in computational finance -- to create market simulations that have analytic validity and can also provide insights into the functioning of asset markets. In particular, I evaluate the model with respect to several traditional financial economic theories like Tobin's separation theorem and capital asset pricing model (CAPM). I also investigate the emergence of different roles played by the agents due to their risk preferences. Furthermore, I perform intervention studies like shocks and explain the outcomes using my model. Finally, I study the effects of noise trading and show that noisy agents converge to a different equilibrium point due to the difference in beliefs. Put together, this thesis presents an agent-based model of asset markets that can be used to study the effects of risks, preferences, and shocks at a systemic level, thereby connecting localized agent and asset characteristics to global or collective outcomes.
Available only to users on the Bowdoin campus.