ABM Applications to Financial Markets

Abstract

Traditional economic theories struggle to explain financial market volatility and the macroeconomy. Traders seldom assume the role of rational optimizers, as described in standard economic models, in financial markets. Boom and bust cycles are widespread, as are firm values based purely on the flashy personalities of CEOs, reckless speculators, and a general sense of irrationality. Clearly, this is not enough to condemn old economic theories as “useless.” In reality, knowing that human behavior leads in self-regulating, stable markets where prices never deviate too far from equilibrium can provide exciting and practical outcomes Instead of evaluating the financial system as a whole and modeling it from the top down, one might examine its agents and see if system-wide characteristics emerge from their interaction. This is extensively used to investigate so-called Complex Systems, which exhibit significant non-linearity and unstable equilibrium states that are easily disrupted by little shocks. Traditional economic theories are effective in illustrating a relatively narrow system state, namely one in which the market is in equilibrium. Alternative techniques, however, backed by complex system theory, may allow for the portrayal of a more generic model in which equilibrium states are a subfield of a larger model. ABM is a valuable approach for simulating complex systems. Bottom-up modeling does not need scientists to approximate the entire system to a differential equation. They could instead mimic the interactions of the system’s single agents. If the agents’ behavioral assumption is right, shared behaviors should “emerge” in the system.