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Heterogeneous Gain Learning and the Dynamics of Asset Prices

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  • Blake LeBaron

    (International Business School, Brandeis University)

Abstract

This paper presents a new agent-based financial market. It is designed to be both simple enough to gain insights into the nature and structure of what is going on at both the agent and macro levels, but remain rich enough to allow for many interesting evolutionary experiments. The model is driven by heterogeneous agents who put varying weights on past information as they design portfolio strategies. It faithfully generates many of the common stylized features of asset markets. It also yields some insights into the dynamics of agent strategies and how they yield market instabilities.

Suggested Citation

  • Blake LeBaron, 2010. "Heterogeneous Gain Learning and the Dynamics of Asset Prices," Working Papers 29, Brandeis University, Department of Economics and International Business School, revised Dec 2010.
    • Handle: RePEc:brd:wpaper:29
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    2. Schmitt, Noemi & Westerhoff, Frank, 2021. "Trend followers, contrarians and fundamentalists: Explaining the dynamics of financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 117-136.
    3. Po-Keng Cheng & Young Shin Kim, 2017. "Speculative bubbles and crashes： Fundamentalists and positive‐feedback trading," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1381370-138, January.
    4. 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.
    5. Pyo, Dong-Jin, 2014. "A Multi-Factor Model of Heterogeneous Traders in a Dynamic Stock Market," Staff General Research Papers Archive 37358, Iowa State University, Department of Economics.
    6. Doris Neuberger & Roger Rissi, 2014. "Macroprudential Banking Regulation: Does One Size Fit All?," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 1(1), pages 5-28, May.
    7. Philip Kostov & Sophia Davidova, 2023. "Smallholders Are Not the Same: Under the Hood of Kosovo Agriculture," Land, MDPI, vol. 12(1), pages 1-16, January.
    8. Aymanns, Christoph & Farmer, J. Doyne, 2015. "The dynamics of the leverage cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 155-179.
    9. Bhattarai, Saroj & Eggertsson, Gauti B. & Schoenle, Raphael, 2018. "Is increased price flexibility stabilizing? Redux," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 66-82.
    10. Andreas Fuster & Benjamin Hebert & David Laibson, 2012. "Natural Expectations, Macroeconomic Dynamics, and Asset Pricing," NBER Macroeconomics Annual, University of Chicago Press, vol. 26(1), pages 1-48.
    11. Kalugala Vidanalage Aruna Shantha, 2019. "Individual Investors’ Learning Behavior and Its Impact on Their Herd Bias: An Integrated Analysis in the Context of Stock Trading," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
    12. Hai-Chuan Xu & Wei Zhang & Xiong Xiong & Wei-Xing Zhou, 2014. "Wealth Share Analysis with “Fundamentalist/Chartist” Heterogeneous Agents," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-11, May.
    13. Georges, Christophre & Pereira, Javier, 2021. "Market stability with machine learning agents," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
    14. 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.
    15. Pyo, Dong-Jin, 2015. "Animal spirits and stock market dynamics," ISU General Staff Papers 201501010800005596, Iowa State University, Department of Economics.
    16. Andrea Giusto, 2015. "Learning to Agree: A New Perspective on Price Drift," Economics Bulletin, AccessEcon, vol. 35(1), pages 276-282.
    17. Chiarella, Carl & He, Xue-Zhong & Zwinkels, Remco C.J., 2014. "Heterogeneous expectations in asset pricing: Empirical evidence from the S&P500," Journal of Economic Behavior & Organization, Elsevier, vol. 105(C), pages 1-16.
    18. Schmitt, Noemi & Westerhoff, Frank, 2017. "On the bimodality of the distribution of the S&P 500's distortion: Empirical evidence and theoretical explanations," Journal of Economic Dynamics and Control, Elsevier, vol. 80(C), pages 34-53.
    19. Georges, Christophre, 2015. "Risk preference and stability under learning," Economics Letters, Elsevier, vol. 132(C), pages 105-108.
    20. Mark Setterfield & Bill Gibson, 2013. "Real and financial crises: A multi-agent approach," Working Papers 1309, Trinity College, Department of Economics, revised Jul 2014.
    21. Thomas J Brennan & Andrew W Lo, 2012. "An Evolutionary Model of Bounded Rationality and Intelligence," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-8, November.
    22. Dong-Jin Pyo, 2017. "A multi-factor model of heterogeneous traders in a dynamic stock market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1416902-141, January.
    23. Goldbaum, David, 2017. "Divergent Behavior in Markets with Idiosyncratic Private Information," Review of Behavioral Economics, now publishers, vol. 4(2), pages 181-213, September.
    24. Blake LeBaron, 2011. "Active and Passive Learning in Agent-based Financial Markets," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 37(1), pages 35-43.
    25. Kostov, Philip & Davidova, Sophia, 2017. "One size does not fit all: an empirical investigation of the Romanian agriculture production function," 91st Annual Conference, April 24-26, 2017, Royal Dublin Society, Dublin, Ireland 258642, Agricultural Economics Society.

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    More about this item

    Keywords

    Learning; Asset Pricing; Financial Time Series; Evolution; Memory;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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