Presentation Title
Selection mechanisms affect volatility in evolving markets
Abstract
Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism as substrate, we analyze the role played by evolutionary selection mechanisms in determining macro-observable market statistics. Specifically, we show that selection mechanisms incorporating a local fitness-proportionate component are associated with high correlation between a micro, risk-aversion parameter and a commonly-used macro-volatility statistic, while a purely quantile-based selection mechanism shows significantly less correlation and is associated with higher absolute levels of fitness (profit) than other selection mechanisms. These results point the way to a possible restructuring of market incentives toward reduction in market-wide worst performance, leading profit-driven agents to behave in ways that are associated with beneficial macro-level outcomes.
Primary Faculty Mentor Name
Chris Danforth
Secondary Mentor Name
Brian Tivnan
Faculty/Staff Collaborators
Michael Vincent Arnold
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Complex Systems
Primary Research Category
Engineering & Physical Sciences
Secondary Research Category
Social Sciences
Selection mechanisms affect volatility in evolving markets
Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism as substrate, we analyze the role played by evolutionary selection mechanisms in determining macro-observable market statistics. Specifically, we show that selection mechanisms incorporating a local fitness-proportionate component are associated with high correlation between a micro, risk-aversion parameter and a commonly-used macro-volatility statistic, while a purely quantile-based selection mechanism shows significantly less correlation and is associated with higher absolute levels of fitness (profit) than other selection mechanisms. These results point the way to a possible restructuring of market incentives toward reduction in market-wide worst performance, leading profit-driven agents to behave in ways that are associated with beneficial macro-level outcomes.