Skip to main content

Complexity of Agents and Complexity of Markets

  • Conference paper
  • First Online:
New Frontiers in Artificial Intelligence (JSAI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2253))

Included in the following conference series:

Abstract

In this study we rethought efficient market hypothesis from a viewpoint of complexity of market participants’ prediction methods and market price’s dynamics, and examined the hypothesis using simulation results of our artificial market model. As a result, we found the two differences from the hypothesis. (a) Complexity of markets was not fixed, but changed with complexity of agents. (b) When agents increased the complexity of their prediction methods, structure of dynamic patterns of market price didn’t disappear, but it can’t be described by equation of any dimensions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chen, S.H., Yeh, C.H. (1996): Genetic programming and the efficient market hypothesis. In Koza, J., Goldberg, D., Fogel, D., eds.: Genetic Programming: Proceedings of the 1st Annual Conference. the MIT Press, 45–53

    Google Scholar 

  2. Chen, S.H., Yeh, C.H., Liao, C.C. (1999): Testing the rational expectations hypothesis with the agent-based model of stock markets. In Proceedings of Internatinal Conference on Artificial Iintelligence 1999. Computer Science Research, Education, and Application Press, 381–387

    Google Scholar 

  3. Chen, S.H., Yeh, C.H., Liao, C.C. (2000): Testing the rational expectations hypothesis with the agent-based model of stock markets. In Papers of the Fourth Annual Conference of The Japan Association for Evolutionary Economics. The Japan Association for Evolutionary Economics, 142–145

    Google Scholar 

  4. de la Maza, M. (1999): Qualitative properties of an agent-based financial market simulation. In: Proceedings of ICAI99. CSREA, 367–373

    Google Scholar 

  5. Joshi, S., Parket, J., Bedau, M.A. (2000): Technical trading creates a prisoner’s dilemma: Results from an agent-based model. In Abu-Mostafa, Y.S., LeBaron, B., Lo, A.W., Weigend, A.S., eds.: Computational Finance 1999, MIT Press, 465–479

    Google Scholar 

  6. Harley, A.C. (1981): Time Series Models. Philip Allan Publishers

    Google Scholar 

  7. Nakajima, Y. (1999): An equivocal property of deterministic, and stochastic processes observed in the economic phenomena. Information Processing Society of Japan, Transaction on Mathematical Modeling and Its Applications 40, (in Japanese).

    Google Scholar 

  8. Nakajima, Y. (2000): Keizai no yuragi to fractal. In Shiozawa, Y., ed.: Houhou to shiteno shinnka. Springer Verlag Tokyo, 207–235, (in Japanese).

    Google Scholar 

  9. Kichiji, N. (2000): Fukajitusei ka deno kitaikeisei to kasetu no shinnka. In Shiozawa, Y., ed.: Houhou to shiteno shinnka. Springer Verlag Tokyo, 173–206, (in Japanese).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Izumi, K. (2001). Complexity of Agents and Complexity of Markets. In: Terano, T., Ohsawa, Y., Nishida, T., Namatame, A., Tsumoto, S., Washio, T. (eds) New Frontiers in Artificial Intelligence. JSAI 2001. Lecture Notes in Computer Science(), vol 2253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45548-5_14

Download citation

  • DOI: https://doi.org/10.1007/3-540-45548-5_14

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43070-4

  • Online ISBN: 978-3-540-45548-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics