IDEAS home Printed from https://ideas.repec.org/r/spr/joecth/v27y2006i3p537-563.html
   My bibliography  Save this item

Asset price bubbles and crashes with near-zero-intelligence traders

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Eduard Krkoska & Klaus Reiner Schenk-Hoppé, 2019. "Herding in Smart-Beta Investment Products," JRFM, MDPI, vol. 12(1), pages 1-14, March.
  2. Kenneth Lomas & Dave Cliff, 2020. "Exploring Narrative Economics: An Agent-Based-Modeling Platform that Integrates Automated Traders with Opinion Dynamics," Papers 2012.08840, arXiv.org.
  3. Dave Cliff, 2024. "Parameterised response zero intelligence traders," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(3), pages 439-492, July.
  4. Baghestanian, Sascha & Walker, Todd B., 2015. "Anchoring in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 116(C), pages 15-25.
  5. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
  6. Ladley, Dan & Schenk-Hoppé, Klaus Reiner, 2009. "Do stylised facts of order book markets need strategic behaviour?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 817-831, April.
  7. Iori, G. & Porter, J., 2012. "Agent-Based Modelling for Financial Markets," Working Papers 12/08, Department of Economics, City University London.
  8. Giusti, Giovanni & Jiang, Janet Hua & Xu, Yiping, 2012. "Eliminating Laboratory Asset Bubbles by Paying Interest on Cash," MPRA Paper 37321, University Library of Munich, Germany.
  9. Makarewicz, Tomasz, 2021. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 626-673.
  10. Miller, Ross M., 2008. "Don't let your robots grow up to be traders: Artificial intelligence, human intelligence, and asset-market bubbles," Journal of Economic Behavior & Organization, Elsevier, vol. 68(1), pages 153-166, October.
  11. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
  12. Frank M. A. Klingert & Matthias Meyer, 2018. "Comparing Prediction Market Mechanisms: An Experiment-Based and Micro Validated Multi-Agent Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-7.
  13. Baghestanian, S. & Lugovskyy, V. & Puzzello, D., 2015. "Traders’ heterogeneity and bubble-crash patterns in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 82-101.
  14. Keisaku HIGASHIDA & Kenta TANAKA & Shunsuke MANAGI, 2018. "Losses on Asset Returns Caused by Perception Gaps of Fundamental Values: Evidence from laboratory experiments," Discussion papers 18008, Research Institute of Economy, Trade and Industry (RIETI).
  15. Ahrens, Steffen & Lustenhouwer, Joep & Tettamanzi, Michele, 2023. "The Stabilizing Effects of Publishing Strategic Central Bank Projections," Macroeconomic Dynamics, Cambridge University Press, vol. 27(3), pages 826-868, April.
  16. Anja Janischewski & Michael Heinrich Baumann, 2025. "What are Asset Price Bubbles? A Survey on Definitions of Financial Bubbles," Chemnitz Economic Papers 065, Department of Economics, Chemnitz University of Technology.
  17. Feldman, Todd & Friedman, Daniel, 2008. "Humans, Robots and Market Crashes: A Laboratory Study ∗," Santa Cruz Department of Economics, Working Paper Series qt4kf382p6, Department of Economics, UC Santa Cruz.
  18. Özge Dilaver & Robert Calvert Jump & Paul Levine, 2018. "Agent‐Based Macroeconomics And Dynamic Stochastic General Equilibrium Models: Where Do We Go From Here?," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1134-1159, September.
  19. Annalisa Fabretti & Tommy Gärling & Stefano Herzel & Martin Holmen, 2017. "Convex incentives in financial markets: an agent-based analysis," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 375-395, November.
  20. Hong, Jieying & Moinas, Sophie & Pouget, Sébastien, 2018. "Learning in Speculative Bubbles: An Experiment," TSE Working Papers 18-882, Toulouse School of Economics (TSE).
  21. Tucker Hybinette Balch & Mahmoud Mahfouz & Joshua Lockhart & Maria Hybinette & David Byrd, 2019. "How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?," Papers 1906.12010, arXiv.org.
  22. Bulent Guler & Volodymyr Lugovskyy & Daniela Puzzello & Steven Tucker, 2021. "Trading Institutions in Experimental Asset Markets: Theory and Evidence," Working Papers in Economics 21/15, University of Waikato.
  23. Hong, Jieying & Moinas, Sophie & Pouget, Sébastien, 2021. "Learning in speculative bubbles: Theory and experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 1-26.
  24. 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.
  25. Lorenzo Cominelli & Gianluca Rho & Caterina Giannetti & Federico Cozzi & Alberto Greco & Graziano A. Manduzio & Philipp Chapkovski & Michalis Drouvelis & Enzo Pasquale Scilingo, 2024. "Emotions in hybrid financial markets," Discussion Papers 2024/311, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
  26. Makarewicz, Tomasz, 2019. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," BERG Working Paper Series 141, Bamberg University, Bamberg Economic Research Group.
  27. Rebecca Westphal & Didier Sornette, 2019. "Market Impact and Performance of Arbitrageurs of Financial Bubbles in An Agent-Based Model," Swiss Finance Institute Research Paper Series 19-29, Swiss Finance Institute.
  28. Lu, Dong & Zhan, Yaosong, 2022. "Over-the-counter versus double auction in asset markets with near-zero-intelligence traders," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
  29. Llacay, Bàrbara & Peffer, Gilbert, 2017. "Impact of value-at-risk models on market stability," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 223-256.
  30. Monira Essa Aloud, 2016. "Profitability of Directional Change Based Trading Strategies: The Case of Saudi Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 87-95.
  31. Francesco Cordoni, 2022. "Multi-Asset Bubbles Equilibrium Price Dynamics," Papers 2206.01468, arXiv.org, revised Sep 2024.
  32. Ross M. Miller, 2012. "The Effect Of Boundary Conditions On Efficiency And Pricing In Double‐Auction Markets With Zero‐Intelligence Agents," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(3), pages 179-188, July.
  33. Jakob Grazzini, 2013. "Information dissemination in an experimentally based agent-based stock market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 179-209, April.
  34. Westphal, Rebecca & Sornette, Didier, 2020. "Market impact and performance of arbitrageurs of financial bubbles in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 1-23.
  35. Xu, Hai-Chuan & Zhang, Wei & Xiong, Xiong & Wang, Xue & Zhou, Wei-Xing, 2021. "The double-edged role of social learning: Flash crash and lower total volatility," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 405-420.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.