IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v29y2007i1p1-12.html
   My bibliography  Save this article

Rate of Return Parity with Robot Asset Traders

Author

Listed:
  • Jason Childs

Abstract

Human populated experimental asset markets produce data with two major qualitative consistencies; finite price bubbles and rate of return parity. Robot traders following different behavioural rules are used to create data that is qualitatively similar to that produced by human subjects in a laboratory setting. A trend pricing component of behaviour is required for robots to generate finite price bubbles. A single arbitrageur in combination with trend pricing and simple profit maximization is required to generate rate of return parity. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Jason Childs, 2007. "Rate of Return Parity with Robot Asset Traders," Computational Economics, Springer;Society for Computational Economics, vol. 29(1), pages 1-12, February.
  • Handle: RePEc:kap:compec:v:29:y:2007:i:1:p:1-12
    DOI: 10.1007/s10614-006-9060-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10614-006-9060-4
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10614-006-9060-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Youssefmir, Michael & Huberman, Bernardo A & Hogg, Tad, 1998. "Bubbles and Market Crashes," Computational Economics, Springer;Society for Computational Economics, vol. 12(2), pages 97-114, October.
    2. Steiglitz, Ken & Shapiro, Daniel, 1998. "Simulating the Madness of Crowds: Price Bubbles in an Auction-Mediated Robot Market," Computational Economics, Springer;Society for Computational Economics, vol. 12(1), pages 35-59, August.
    3. Timothy N. Cason & Daniel Friedman, 1997. "Price Formation in Single Call Markets," Econometrica, Econometric Society, vol. 65(2), pages 311-346, March.
    4. Sunder, S., 1992. "Experimental Asset Markets: A Survey," GSIA Working Papers 1992-19, Carnegie Mellon University, Tepper School of Business.
    5. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    6. Robert Moir, 1998. "A Monte Carlo Analysis of the Fisher Randomization Technique: Reviving Randomization for Experimental Economists," Experimental Economics, Springer;Economic Science Association, vol. 1(1), pages 87-100, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lange, Andreas & Ross, Johannes, 2024. "Internalizing match-dependent externalities," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 356-378.
    2. Nuzzo, Simone & Morone, Andrea, 2017. "Asset markets in the lab: A literature review," Journal of Behavioral and Experimental Finance, Elsevier, vol. 13(C), pages 42-50.
    3. Palan, Stefan, 2010. "Digital options and efficiency in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 75(3), pages 506-522, September.
    4. Morone, Andrea & Nuzzo, Simone, 2015. "Market Efficiency, Trading Institutions and Information Mirages: evidence from an experimental asset market," MPRA Paper 67448, University Library of Munich, Germany.
    5. Todd Feldman & Daniel Friedman, 2010. "Human and Artificial Agents in a Crash-Prone Financial Market," Computational Economics, Springer;Society for Computational Economics, vol. 36(3), pages 201-229, October.
    6. Marco Licalzi & Paolo Pellizzari, 2003. "Fundamentalists clashing over the book: a study of order-driven stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 3(6), pages 470-480.
    7. Jean Paul Rabanal & Olga A. Rabanal, 2015. "A Simulation on the Evolution of Markets: Call Market, Decentralized and Posted Offer," Working Papers 34, Peruvian Economic Association.
    8. Theodore Turocy & Elizabeth Watson & Raymond Battalio, 2007. "Framing the first-price auction," Experimental Economics, Springer;Economic Science Association, vol. 10(1), pages 37-51, March.
    9. Gode, Dhananjay (Dan) K. & Sunder, Shyam, 2004. "Double auction dynamics: structural effects of non-binding price controls," Journal of Economic Dynamics and Control, Elsevier, vol. 28(9), pages 1707-1731, July.
    10. Slembeck, Tilman & Tyran, Jean-Robert, 2004. "Do institutions promote rationality?: An experimental study of the three-door anomaly," Journal of Economic Behavior & Organization, Elsevier, vol. 54(3), pages 337-350, July.
    11. Jens Grossklags & Carsten Schmidt, 2002. "Artificial Software Agents on Thin Double Auction Markets - A Human Trader Experiment," Papers on Strategic Interaction 2002-45, Max Planck Institute of Economics, Strategic Interaction Group.
    12. Sean Crockett, 2013. "Price Dynamics In General Equilibrium Experiments," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 421-438, July.
    13. Koye Somefun & Philip Mirowski, 1999. "Towards an Automata Approach of (Institutional) Economics," Computing in Economics and Finance 1999 213, Society for Computational Economics.
    14. Timothy Cason & Daniel Friedman, 1999. "Learning in a Laboratory Market with Random Supply and Demand," Experimental Economics, Springer;Economic Science Association, vol. 2(1), pages 77-98, August.
    15. Te Bao & Elizaveta Nekrasova & Tibor Neugebauer & Yohanes E. Riyanto, 2022. "Algorithmic trading in experimental markets with human traders: A literature survey," Chapters, in: Sascha Füllbrunn & Ernan Haruvy (ed.), Handbook of Experimental Finance, chapter 23, pages 302-322, Edward Elgar Publishing.
    16. Andrea Morone & Simone Nuzzo, 2019. "Market efficiency, trading institutions and information mirages: evidence from a laboratory asset market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(2), pages 317-344, June.
    17. Daniel Fricke & Thomas Lux, 2015. "The effects of a financial transaction tax in an artificial financial market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(1), pages 119-150, April.
    18. Crockett, Sean & Friedman, Daniel & Oprea, Ryan, 2017. "Aggregation and convergence in experimental general equilibrium economies constructed from naturally occurring preferences," Discussion Papers, Research Professorship Market Design: Theory and Pragmatics SP II 2017-501, WZB Berlin Social Science Center.
    19. Berg, Joyce E. & Rietz, Thomas A., 2019. "Longshots, overconfidence and efficiency on the Iowa Electronic Market," International Journal of Forecasting, Elsevier, vol. 35(1), pages 271-287.
    20. Daniel Sutter & Daniel J. Smith, 2017. "Coordination in disaster: Nonprice learning and the allocation of resources after natural disasters," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 30(4), pages 469-492, December.

    More about this item

    Keywords

    interest rate parity; rate of return parity; arbitrage; C89; F3; G12;
    All these keywords.

    JEL classification:

    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • F3 - International Economics - - International Finance
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:29:y:2007:i:1:p:1-12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.