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The Investigation of the Agent in the Artificial Market

  • Takahiro Kitakubo
  • Yuhsuke Koyama
  • Hiroshi Deguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3397)

Abstract

In this paper, we investigate the investment strategy in the artificial market called U-Mart, which is designed to provide a common test bed for researchers in the fields of economics and information sciences. UMIE is the international experiment of U-Mart as a contests of trading agents. We attended UMIE 2003 and 2004, and our agent won the championship in both experiments. We examin why this agent is strong in UMIE environment. The strategy of this agent is called “on-line learning” or “real-time learning”. Concretely, the agent exploits and forecasts futures price fluctuations by means of identifying the environment in reinforcement learning.

We examined an efficiency of price forecasting in the classified environment. To examine the efficacy of it, we executed experiments 1000 times with UMIE open-type simulation standard toolkits, and we verified that forecasting futures price fluctuation in our strategy is useful for better trading.

Keywords

U-Mart Artificial Market Reinforcement Learning 

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References

  1. 1.
  2. 2.
    Matsui, H., Nakajima, Y., Sato, H.: Performance of Machine Agents Submitted to U-Mart International Experiment 2004(UMIE 2004). In: Proceedings of the Second International Workshop on Agent-based Approaches in Economic and Social Complex Systems, AESCS 2004 (2004)Google Scholar
  3. 3.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning). MIT Press, CambridgeGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Takahiro Kitakubo
    • 1
  • Yuhsuke Koyama
    • 1
  • Hiroshi Deguchi
    • 1
  1. 1.Department of Computational Systems and Science, Interdisciplinary Graduate Scholl of Science and EngineeringTokyo Institute of Technology 

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