IDEAS home Printed from https://ideas.repec.org/r/cty/dpaper/10-05.html
   My bibliography  Save this item

Herding effects in order driven markets: The rise and fall of gurus

Citations

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


Cited by:

  1. Misha Perepelitsa & Ilya Timofeyev, 2022. "Self-sustained price bubbles driven by digital currency innovations and adaptive market behavior," SN Business & Economics, Springer, vol. 2(3), pages 1-15, March.
  2. Goldbaum, David, 2021. "The origins of influence," Economic Modelling, Elsevier, vol. 97(C), pages 380-396.
  3. Blaurock, Ivonne & Schmitt, Noemi & Westerhoff, Frank, 2018. "Market entry waves and volatility outbursts in stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 19-37.
  4. Daniele Giachini, 2018. "Rationality and Asset Prices under Belief Heterogeneity," LEM Papers Series 2018/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  5. Fabio Della Rossa & Lorenzo Giannini & Pietro DeLellis, 2020. "Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
  6. Mark Setterfield & Bill Gibson, 2013. "Real and financial crises: A multi-agent approach," Working Papers 1309, Trinity College, Department of Economics, revised Jul 2014.
  7. Lu, Jingen & Chen, Xiaohong & Liu, Xiaoxing, 2018. "Stock market information flow: Explanations from market status and information-related behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 837-848.
  8. Matthew Oldham, 2019. "Understanding How Short-Termism and a Dynamic Investor Network Affects Investor Returns: An Agent-Based Perspective," Complexity, Hindawi, vol. 2019, pages 1-21, July.
  9. Riccetti, Luca & Russo, Alberto & Gallegati, Mauro, 2016. "Stock market dynamics, leveraged network-based financial accelerator and monetary policy," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 509-524.
  10. Galariotis, Emilios C. & Rong, Wu & Spyrou, Spyros I., 2015. "Herding on fundamental information: A comparative study," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 589-598.
  11. Jiewang Chu & Jiaxuan Li, 2022. "The Composition and Operation Mechanism of Digital Entrepreneurial Ecosystem: A Study of Hangzhou Yunqi Town as an Example," Sustainability, MDPI, vol. 14(24), pages 1-19, December.
  12. Galariotis, Emilios C. & Krokida, Styliani-Iris & Spyrou, Spyros I., 2016. "Bond market investor herding: Evidence from the European financial crisis," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 367-375.
  13. Lenzu, Simone & Tedeschi, Gabriele, 2012. "Systemic risk on different interbank network topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4331-4341.
  14. Iori, G. & Porter, J., 2012. "Agent-Based Modelling for Financial Markets," Working Papers 12/08, Department of Economics, City University London.
  15. Alessio Emanuele Biondo, 2020. "Information versus imitation in a real-time agent-based model of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(3), pages 613-631, July.
  16. Alessio Emanuele Biondo, 2018. "Order book microstructure and policies for financial stability," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 35(1), pages 196-218, March.
  17. Pascal Seppecher & Isabelle Salle, 2015. "Deleveraging crises and deep recessions: a behavioural approach," Applied Economics, Taylor & Francis Journals, vol. 47(34-35), pages 3771-3790, July.
  18. Patrick Chang, 2020. "Fourier instantaneous estimators and the Epps effect," Papers 2007.03453, arXiv.org, revised Sep 2020.
  19. Wang, Guocheng & Wang, Yanyi, 2018. "Herding, social network and volatility," Economic Modelling, Elsevier, vol. 68(C), pages 74-81.
  20. Alessio Emanuele Biondo, 2019. "Order book modeling and financial stability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 469-489, September.
  21. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 991-1020, April.
  22. Vivien Lespagnol & Juliette Rouchier, 2014. "Trading volume and market efficiency: an Agent Based Model with heterogenous knowledge about fundamentals," AMSE Working Papers 1419, Aix-Marseille School of Economics, France, revised May 2014.
  23. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW Kiel).
  24. Bao, Te & Corgnet, Brice & Hanaki, Nobuyuki & Riyanto, Yohanes E. & Zhu, Jiahua, 2023. "Predicting the unpredictable: New experimental evidence on forecasting random walks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
  25. Li, Zhuolei & Diao, Xundi & Wu, Chongfeng, 2022. "The influence of mobile trading on return dispersion and herding behavior," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
  26. Biondo, Alessio Emanuele, 2018. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-21.
  27. Chang Sheng-Kai, 2014. "Herd behavior, bubbles and social interactions in financial markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(1), pages 89-101, February.
  28. Jan Polach & Jiri Kukacka, 2019. "Prospect Theory in the Heterogeneous Agent Model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(1), pages 147-174, March.
  29. Luisanna Cocco & Michele Marchesi, 2016. "Modeling and Simulation of the Economics of Mining in the Bitcoin Market," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-31, October.
  30. Muskan Sachdeva & Ritu Lehal & Sanjay Gupta & Aashish Garg, 2021. "What make investors herd while investing in the Indian stock market? A hybrid approach," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 15(1), pages 19-37, September.
  31. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
  32. Rocco Caferra & Gabriele Tedeschi & Andrea Morone, 2023. "Agents interaction and price dynamics: evidence from the laboratory," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(2), pages 251-274, April.
  33. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
  34. Bargigli, Leonardo & Tedeschi, Gabriele, 2014. "Interaction in agent-based economics: A survey on the network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 1-15.
  35. Simone Berardi & Gabriele Tedeschi, 2016. "How banks’ strategies influence financial cycles: An approach to identifying micro behavior," Working Papers 2016/24, Economics Department, Universitat Jaume I, Castellón (Spain).
  36. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2015. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 1-25.
  37. Егорова Людмила Геннадьевна, 2014. "Эффективность Торговых Стратегий Мелких Трейдеров," Проблемы управления, CyberLeninka;Общество с ограниченной ответственностью "СенСиДат-Контрол", issue 5, pages 34-41.
  38. Ibrahim Filiz & Thomas Nahmer & Markus Spiwoks & Kilian Bizer, 2018. "Portfolio diversification: the influence of herding, status-quo bias, and the gambler’s fallacy," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(2), pages 167-205, May.
  39. Brzezicka Justyna & Wisniewski Radosław, 2014. "Price Bubble In The Real Estate Market - Behavioral Aspects," Real Estate Management and Valuation, Sciendo, vol. 22(1), pages 1-14, March.
  40. Tedeschi, Gabriele & Recchioni, Maria Cristina & Berardi, Simone, 2019. "An approach to identifying micro behavior: How banks’ strategies influence financial cycles," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 329-346.
  41. Zhang, Junhuan, 2018. "Influence of individual rationality on continuous double auction markets with networked traders," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 353-392.
  42. Junhuan Zhang & Peter McBurney & Katarzyna Musial, 2018. "Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders," Review of Quantitative Finance and Accounting, Springer, vol. 50(1), pages 301-352, January.
  43. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda & Dirk Helbing, 2013. "Are Random Trading Strategies More Successful than Technical Ones?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-13, July.
  44. Hazem Krichene & Mhamed-Ali El-Aroui, 2018. "Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 511-535, October.
  45. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Post-Print hal-02084910, HAL.
  46. Christopher M Wray & Steven R Bishop, 2016. "A Financial Market Model Incorporating Herd Behaviour," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-28, March.
  47. Vivien Lespagnol & Juliette Rouchier, 2015. "What Is the Impact of Heterogeneous Knowledge About Fundamentals on Market Liquidity and Efficiency: An ABM Approach," Lecture Notes in Economics and Mathematical Systems, in: Frédéric Amblard & Francisco J. Miguel & Adrien Blanchet & Benoit Gaudou (ed.), Advances in Artificial Economics, edition 127, pages 105-117, Springer.
  48. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046.
  49. Humayun Kabir, M. & Shakur, Shamim, 2018. "Regime-dependent herding behavior in Asian and Latin American stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 60-78.
  50. Liudmila G. Egorova, 2014. "The Effectiveness Of Different Trading Strategies For Price-Takers," HSE Working papers WP BRP 29/FE/2014, National Research University Higher School of Economics.
  51. Gabriele Tedeschi & Stefania Vitali & Mauro Gallegati, 2014. "The dynamic of innovation networks: a switching model on technological change," Journal of Evolutionary Economics, Springer, vol. 24(4), pages 817-834, September.
  52. A. E. Biondo & A. Pluchino & A. Rapisarda & D. Helbing, 2013. "Are random trading strategies more successful than technical ones?," Papers 1303.4351, arXiv.org, revised Jul 2013.
  53. Grilli, Ruggero & Tedeschi, Gabriele & Gallegati, Mauro, 2020. "Business fluctuations in a behavioral switching model: Gridlock effects and credit crunch phenomena in financial networks," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
  54. Maria Elvira Mancino & Maria Cristina Recchioni, 2015. "Fourier Spot Volatility Estimator: Asymptotic Normality and Efficiency with Liquid and Illiquid High-Frequency Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-33, September.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.