A well-functioning financial system is critical for the operation of a complex global economy, and finance itself represents a major sector of economic activity. The financial system is in many respects a computational device: allocating capital resources based on beliefs about current and future productivity, processing payments through a distributed account network, and making credit decisions based on trust and expectations of future payment capacity. Moreover, financial decisions are increasingly automated, from algorithmic trading in asset markets to credit and underwriting policies executed automatically through hand-coded or statistically derived rules. Our research aims to model and analyze these financial functions, emphasizing the interactions of strategic choices made by distributed agents playing various roles in the financial system. To these ends, we employ a combination of agent-based simulation modeling and game-theoretic analysis.

Work to date has focused in two main areas:

  1. Implications of algorithmic trading on financial markets. This includes modeling high-frequency and other algorithmic trading strategies, in a variety of market configurations. Questions include the relation of algorithmic strategies and contextual features to market performance, and how to design market rules and regulations to promote economic efficiency and financial stability.
  2. Modeling networks of financial credit relationships. We are developing a comprehensive framework able to capture representations of distributed trust, payment processing through combinations of credit and debt obligations, and systemic effects of complex webs of credit relationships. Questions include how financial credit networks are formed and evolve over time, and implications of alternative credit policies and regulations on financial stability.

Related Projects and Publications: