Abstract

Technological advancement has led to an increase in the number and type of trading venues and a diversification of goods traded. These changes have re-emphasized the importance of understanding the effects of market competition: does proliferation of trading venues and increased competition lead to dominance of a single market or coexistence of multiple markets? In this paper, we address these questions in a stylized model of zero-intelligence traders who make repeated decisions at which of three available markets to trade. We analyse the model numerically and analytically and find that the traders’ decision parameters—memory length and how strongly decisions are based on past success—make the key difference between consolidated and fragmented steady states of the population of traders. All three markets coexist with equal shares of traders only when either learning is too weak and traders choose randomly, or when markets are identical. In the latter case, the population of traders fragments across the markets. With different markets, we note that market dominance is the more typical scenario. Overall we show that, contrary to previous research emphasizing the role of traders’ heterogeneity, market coexistence can emerge simply as a consequence of co-adaptation of an initially homogeneous population of traders.

Highlights

  • Technological advancement has led to an increase in the number and type of trading venues and a diversification of goods traded

  • These changes have re-emphasized the importance of understanding the effects of market competition: does proliferation of trading venues and increased competition lead to dominance of a single market or coexistence of multiple markets? In this paper, we address these questions in a stylized model of zero-intelligence traders who make repeated decisions at which of three available markets to trade

  • To tackle this question of market coexistence versus single market dominance, we build on previous work [5,6,7,8] where we introduced and analysed a system consisting of double auction markets and a large number of traders choosing between them

Read more

Summary

Agent-based model

We summarize the basic assumptions and properties of the model introduced in [5,6,8] and extend it to include multiple markets

Traders
Markets
Learning rules
Numerical simulations
Analysis
Three fair markets
Exploration of the parameter space: markets with different biases
Two symmetrically biased markets and one fair market
Two symmetric markets and one biased market
Markets without symmetry
General number of markets M
Summary and outlook
Freidlin–Wentzell theory
Findings
Finding the minimal action path numerically
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.