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Confidence and the Stock Market: An Agent-Based Approach

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  • Mario A Bertella
  • Felipe R Pires
  • Ling Feng
  • Harry Eugene Stanley

Abstract

Using a behavioral finance approach we study the impact of behavioral bias. We construct an artificial market consisting of fundamentalists and chartists to model the decision-making process of various agents. The agents differ in their strategies for evaluating stock prices, and exhibit differing memory lengths and confidence levels. When we increase the heterogeneity of the strategies used by the agents, in particular the memory lengths, we observe excess volatility and kurtosis, in agreement with real market fluctuations—indicating that agents in real-world financial markets exhibit widely differing memory lengths. We incorporate the behavioral traits of adaptive confidence and observe a positive correlation between average confidence and return rate, indicating that market sentiment is an important driver in price fluctuations. The introduction of market confidence increases price volatility, reflecting the negative effect of irrationality in market behavior.

Suggested Citation

  • Mario A Bertella & Felipe R Pires & Ling Feng & Harry Eugene Stanley, 2014. "Confidence and the Stock Market: An Agent-Based Approach," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
    • Handle: RePEc:plo:pone00:0083488
    DOI: 10.1371/journal.pone.0083488
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    1. Wolski, Marcin & van de Leur, Michiel, 2016. "Interbank loans, collateral and modern monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 388-416.
    2. Javier Rojo-Suárez & Ana Belén Alonso-Conde, 2020. "Impact of consumer confidence on the expected returns of the Tokyo Stock Exchange: A comparative analysis of consumption and production-based asset pricing models," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.
    3. Roberto Mota Navarro & Hern'an Larralde Ridaura, 2016. "A detailed heterogeneous agent model for a single asset financial market with trading via an order book," Papers 1601.00229, arXiv.org, revised Jul 2016.
    4. Yan Li & Bo Zheng & Ting-Ting Chen & Xiong-Fei Jiang, 2017. "Fluctuation-driven price dynamics and investment strategies," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-15, December.
    5. Jonathan F Schulz & Christian Thöni, 2016. "Overconfidence and Career Choice," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-8, January.
    6. Phooi M’ng, Jacinta Chan, 2018. "Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 336-345.
    7. Zhou, Wei & Zhong, Guang-Yan & Li, Jiang-Cheng, 2022. "Stability of financial market driven by information delay and liquidity in delay agent-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    8. Khaldoun Khashanah & Talal Alsulaiman, 2017. "Connectivity, Information Jumps, and Market Stability: An Agent-Based Approach," Complexity, Hindawi, vol. 2017, pages 1-16, August.
    9. Thiago Christiano Silva & Benjamin Miranda Tabak & Idamar Magalhães Ferreira, 2019. "Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies," Complexity, Hindawi, vol. 2019, pages 1-14, December.
    10. Roberto Mota Navarro & Hernán Larralde, 2017. "A detailed heterogeneous agent model for a single asset financial market with trading via an order book," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-27, February.
    11. Binghui Wu & Tingting Duan, 2019. "Nonlinear Dynamics Characteristic of Risk Contagion in Financial Market Based on Agent Modeling and Complex Network," Complexity, Hindawi, vol. 2019, pages 1-12, June.
    12. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    13. Li, Jiang-Cheng & Tao, Chen & Li, Hai-Feng, 2022. "Dynamic forecasting performance and liquidity evaluation of financial market by Econophysics and Bayesian methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    14. Songtao Wu & Jianmin He & Chao Wang, 2017. "Effects of Common Factors on Dynamics of Stocks Traded by Investors with Limited Information Capacity," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-15, September.
    15. Ibrahim Filiz & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2021. "Sticky Stock Market Analysts," JRFM, MDPI, vol. 14(12), pages 1-27, December.
    16. Goykhman, Mikhail, 2017. "Wealth dynamics in a sentiment-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 488(C), pages 132-148.
    17. Bertella, Mario A. & Silva, Jonathas N. & Stanley, H. Eugene, 2020. "Loss aversion, overconfidence and their effects on a virtual stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).

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