Robust methods for tracking intelligent agents playing in an artificial financial market

Nachi Gupta, Raphael Hauser, Neil F. Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

When analyzing financial time-series for predictability, the norm has been to find trends and patterns directly in the series despite the inherent dynamical system apparent at the individual agent level. This underlying buy and sell model provides more information than the time-series alone. We provide a methodology for finding pockets of predictability in a financial time-series using a multiagent market model and an empirical study to illustrate convergence of these methods.

Original languageEnglish (US)
Title of host publicationAAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems
Pages1106-1108
Number of pages3
DOIs
StatePublished - Dec 1 2007
Event6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07 - Honolulu, HI, United States
Duration: May 14 2008May 18 2008

Publication series

NameProceedings of the International Conference on Autonomous Agents

Other

Other6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07
CountryUnited States
CityHonolulu, HI
Period5/14/085/18/08

Keywords

  • Econophysics
  • Inequality constrained Kalman filtering
  • Multi-agent games

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Theoretical Computer Science

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  • Cite this

    Gupta, N., Hauser, R., & Johnson, N. F. (2007). Robust methods for tracking intelligent agents playing in an artificial financial market. In AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 1106-1108). [176] (Proceedings of the International Conference on Autonomous Agents). https://doi.org/10.1145/1329125.1329338