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Hidden forces and fluctuations from moving averages: A test study

Author

Listed:
  • Alfi, V.
  • Coccetti, F.
  • Marotta, M.
  • Pietronero, L.
  • Takayasu, M.

Abstract

The possibility that price dynamics is affected by its distance from a moving average has been recently introduced as new statistical tool. The purpose is to identify the tendency of the price dynamics to be attractive or repulsive with respect to its own moving average. We consider a number of tests for various models which clarify the advantages and limitations of this new approach. The analysis leads to the identification of an effective potential with respect to the moving average. Its specific implementation requires a detailed consideration of various effects which can alter the statistical methods used. However, the study of various model systems shows that this approach is indeed suitable to detect hidden forces in the market which go beyond usual correlations and volatility clustering.

Suggested Citation

  • Alfi, V. & Coccetti, F. & Marotta, M. & Pietronero, L. & Takayasu, M., 2006. "Hidden forces and fluctuations from moving averages: A test study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 30-37.
    • Handle: RePEc:eee:phsmap:v:370:y:2006:i:1:p:30-37
    DOI: 10.1016/j.physa.2006.04.113
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    References listed on IDEAS

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    1. V. Alfi & F. Coccetti & A. Petri & L. Pietronero, 2006. "Roughness and Finite Size Effect in the NYSE Stock-Price Fluctuations," Papers physics/0602052, arXiv.org.
    2. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169, October.
    3. R. Baviera & M. Pasquini & J. Raboanary & M. Serva, 2002. "Moving Averages And Price Dynamics," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 5(06), pages 575-583.
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    Cited by:

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    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. Alfi, V. & De Martino, A. & Pietronero, L. & Tedeschi, A., 2007. "Detecting the traders’ strategies in minority–majority games and real stock-prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 1-8.
    7. V. Alfi & L. Pietronero & A. Zaccaria, 2008. "Minimal Agent Based Model For The Origin And Self-Organization Of Stylized Facts In Financial Markets," Papers 0807.1888, arXiv.org.
    8. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.

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