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On the bimodality of the distribution of the S&P 500's distortion: Empirical evidence and theoretical explanations

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  • Schmitt, Noemi
  • Westerhoff, Frank

Abstract

After showing that the distribution of the S&P 500's distortion, i.e. the log difference between its real stock market index and its real fundamental value, is bimodal, we demonstrate that agentbased financial market models may explain this puzzling observation. Within these models, speculators apply technical and fundamental analysis to predict asset prices. Since destabilizing technical trading dominates the market near the fundamental value, asset prices tend to be either overvalued or undervalued. Interestingly, the bimodality of the distribution of the S&P 500's distortion confirms an implicit prediction of a number of seminal agent-based financial market models.

Suggested Citation

  • Schmitt, Noemi & Westerhoff, Frank, 2017. "On the bimodality of the distribution of the S&P 500's distortion: Empirical evidence and theoretical explanations," BERG Working Paper Series 119, Bamberg University, Bamberg Economic Research Group.
    • Handle: RePEc:zbw:bamber:119
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    Cited by:

    1. 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.
    2. Huang, Weihong & Chen, Zhenxi, 2020. "Modelling contagion of financial crises," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    3. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of an agent-based market-model with a matching engine," Papers 2108.07806, arXiv.org, revised Aug 2021.
    4. Jochen Jungeilges & Elena Maklakova & Tatyana Perevalova, 2022. "Stochastic sensitivity of bull and bear states," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(1), pages 165-190, January.
    5. Ivan Jericevich & Murray McKechnie & Tim Gebbie, 2021. "Calibrating an adaptive Farmer-Joshi agent-based model for financial markets," Papers 2104.09863, arXiv.org.
    6. 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.
    7. Noemi Schmitt & Ivonne Schwartz & Frank Westerhoff, 2022. "Heterogeneous speculators and stock market dynamics: a simple agent-based computational model," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1263-1282, October.
    8. Goldbaum, David, 2017. "Divergent Behavior in Markets with Idiosyncratic Private Information," Review of Behavioral Economics, now publishers, vol. 4(2), pages 181-213, September.
    9. Lux, Thomas, 2020. "Can heterogeneous agent models explain the alleged mispricing of the S&P 500?," Economics Working Papers 2020-03, Christian-Albrechts-University of Kiel, Department of Economics.
    10. Adam Majewski & Stefano Ciliberti & Jean-Philippe Bouchaud, 2018. "Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model," Papers 1807.11751, arXiv.org.
    11. Majewski, Adam A. & Ciliberti, Stefano & Bouchaud, Jean-Philippe, 2020. "Co-existence of trend and value in financial markets: Estimating an extended Chiarella model," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    12. 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.

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    More about this item

    Keywords

    stock market dynamics; bubbles and crashes; chartists and fundamentalists; nonlinear dynamics; bimodality tests; time series analysis;
    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

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