Estimation of an agent-based model of investor sentiment formation in financial markets
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DOI: 10.1016/j.jedc.2012.03.012
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Citations
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Cited by:
- Gontis, V. & Havlin, S. & Kononovicius, A. & Podobnik, B. & Stanley, H.E., 2016. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1091-1102.
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- Jørgen Vitting Andersen & Ioannis Vrontos & Petros Dellaportas & Serge Galam, 2014. "Communication impacting financial markets," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00982959, HAL.
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- Thomas Lux, 2013. "Inference for systems of stochastic differential equations from discretely sampled data: a numerical maximum likelihood approach," Annals of Finance, Springer, vol. 9(2), pages 217-248, May.
- Xue-Zhong He & Youwei Li, 2017.
"The adaptiveness in stock markets: testing the stylized facts in the DAX 30,"
Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1071-1094, November.
- Xue-Zhong He & Youwei Li, 2015. "The Adaptiveness in Stock Markets: Testing the Stylized Facts in the Dax 30," Research Paper Series 364, Quantitative Finance Research Centre, University of Technology, Sydney.
- ARATA Yoshiyuki & KIMURA Yosuke & MURAKAMI Hiroki, 2015. "Macroeconomic Consequences of Lumpy Investment under Uncertainty," Discussion papers 15120, Research Institute of Economy, Trade and Industry (RIETI).
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"A method for agent-based models validation,"
Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 125-141.
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- Wang, Jie & Wang, Jun, 2020. "Cross-correlation complexity and synchronization of the financial time series on Potts dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
- Li, Da-Ye & Nishimura, Yusaku & Men, Ming, 2014. "Fractal markets: Liquidity and investors on different time horizons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 144-151.
- Vygintas Gontis & Shlomo Havlin & Aleksejus Kononovicius & Boris Podobnik & H. Eugene Stanley, 2015. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Papers 1507.05203, arXiv.org, revised Oct 2016.
- Zhenxi Chen & Weihong Huang & Huanhuan Zheng, 2018.
"Estimating heterogeneous agents behavior in a two-market financial system,"
Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 491-510, October.
- Chen, Zhenxi & Huang, Weihong & Zheng, Huanhuan, 2015. "Estimating heterogeneous agents behavior in a two-market financial system," FinMaP-Working Papers 48, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
- Lucas Fievet & Didier Sornette, 2018. "Calibrating emergent phenomena in stock markets with agent based models," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
- 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.
- Vygintas Gontis & Aleksejus Kononovicius, 2017. "The consentaneous model of the financial markets exhibiting spurious nature of long-range memory," Papers 1712.05121, arXiv.org, revised Feb 2018.
- Gontis, V. & Kononovicius, A., 2018. "The consentaneous model of the financial markets exhibiting spurious nature of long-range memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 1075-1083.
- Lux, Thomas, 2012. "Inference for systems of stochastic differential equations from discretely sampled data: A numerical maximum likelihood approach," Kiel Working Papers 1781, Kiel Institute for the World Economy (IfW).
- Daye Li & Rongrong Li & Qiankun Sun, 2017. "How the heterogeneity in investment horizons affects market trends," Applied Economics, Taylor & Francis Journals, vol. 49(15), pages 1473-1482, March.
- Zheng, Min & Liu, Ruipeng & Li, Youwei, 2018.
"Long memory in financial markets: A heterogeneous agent model perspective,"
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- Zheng, Min & Liu, Ruipeng & Li, Youwei, 2018. "Long memory in financial markets: A heterogeneous agent model perspective," MPRA Paper 84886, University Library of Munich, Germany.
- Guillaume Coqueret, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02000726, HAL.
- F. M. Stefan & A. P. F. Atman, 2017. "Asymmetric return rates and wealth distribution influenced by the introduction of technical analysis into a behavioral agent based model," Papers 1711.08282, arXiv.org.
- Shi, Yong & Tang, Ye-ran & Long, Wen, 2019. "Sentiment contagion analysis of interacting investors: Evidence from China’s stock forum," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 246-259.
- Hongli Niu & Jun Wang, 2014. "Phase and multifractality analyses of random price time series by finite-range interacting biased voter system," Computational Statistics, Springer, vol. 29(5), pages 1045-1063, October.
- Coqueret, Guillaume, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 180-201.
- Guillaume Coqueret, 2016. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02088097, HAL.
- Chen, Zhenxi, 2014. "Estimating heterogeneous agents behavior with different investment horizons in stock markets," FinMaP-Working Papers 5, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
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More about this item
Keywords
Opinion formation; Social interaction; Investor sentiment;All these keywords.
JEL classification:
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
Statistics
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