Primary Faculty Mentor Name

Chris Danforth

Project Collaborators

Michael Vincent Arnold

Secondary Mentor Name

Brian Tivnan

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

Presentation Title

Selection mechanisms affect volatility in evolving markets

Time

1:00 PM

Location

Silver Maple Ballroom - Engineering & Physical Sciences

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.

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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.