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:
- 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.
- 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:
- Understanding the Implications of Advanced AI on Financial Markets
- Market Making with Learned Beta Policies
- A Financial Market Simulation Environment for Trading Agents Using Deep Reinforcement Learning
- The Effect of Liquidity on the Spoofability of Financial Markets
- Fraud Risk Mitigation in Real-Time Payments: A Strategic Agent-Based Analysis
- Learning to Manipulate a Financial Benchmark
- Stability Effects of Arbitrage in Exchange Traded Funds: An Agent-Based Model
- Timing is Money: The Impact of Arrival Order in Beta-Bernoulli Prediction Markets
- An Agent-Based Model of Strategic Adoption of Real-Time Payments
- Designing a Combinatorial Financial Options Market
- Spoofing the Limit Order Book: A Strategic Agent-Based Analysis
- Log-time Prediction Markets for Interval Securities
- A Strategic Analysis of Portfolio Compression
- An Agent-Based Model of Financial Benchmark Manipulation
- Learning-Based Trading Strategies in the Face of Market Manipulation
- Market manipulation: An adversarial learning framework for detection and evasion
- Generating realistic stock market order streams
- A Cloaking Mechanism to Mitigate Market Manipulation
- Evaluating the stability of non-adaptive trading in continuous double auctions
- Accounting for strategic response in an agent-based model of financial regulation
- Shading and efficiency in limit-order markets
- Empirical mechanism design for optimizing clearing interval in frequent call markets
- Spoofing the limit order book: An agent-based model
- Strategic agent-based modeling of financial markets
- Ethical issues for autonomous trading agents
- Latency arbitrage in fragmented markets: A strategic agent-based analysis
- Strategic Payment Routing in Financial Credit Networks
- Strategic Market Choice: Frequent Call Markets vs. Continuous Double Auctions for Fast and Slow Traders
- Welfare Effects of Market Making in Continuous Double Auctions
- Latency Arbitrage, Market Fragmentation, and Efficiency: A Two-Market Model
- An empirical game-theoretic analysis of credit network formation
- Strategic Formation of Credit Networks
- Trading Agents
- Asset pricing under ambiguous information: An empirical game-theoretic analysis
- Stronger CDA Strategies through Empirical Game-Theoretic Analysis and Reinforcement Learning