IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2107.03857.html
   My bibliography  Save this paper

Financial Markets and the Phase Transition between Water and Steam

Author

Listed:
  • Christof Schmidhuber

Abstract

Motivated by empirical observations on the interplay of trends and reversion, a lattice gas model of financial markets is presented. The shares of an asset are modeled by gas molecules that are distributed across a hidden social network of investors. The model is equivalent to the Ising model on this network, whose magnetization represents the deviation of the asset price from its value. Moreover, the system should drive itself to its critical temperature in efficient markets. There, it is characterized by universal critical exponents, in analogy with the second-order phase transition between water and steam. These critical exponents imply predictions for the auto-correlations of financial market returns and for Hurst exponents. For a simple network topology, consistency with empirical observations implies a fractal network dimension near 3, and a correlation time at least as long as the economic cyle. To also explain the observed market auto-correlations at intermediate scales, the model should be extended beyond the critical domain, to other network topologies, and to other models of critical dynamics.

Suggested Citation

  • Christof Schmidhuber, 2021. "Financial Markets and the Phase Transition between Water and Steam," Papers 2107.03857, arXiv.org, revised Dec 2021.
  • Handle: RePEc:arx:papers:2107.03857
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2107.03857
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jean-Philippe Bouchaud & Rama Cont, 1998. "A Langevin approach to stock market fluctuations and crashes," Science & Finance (CFM) working paper archive 500027, Science & Finance, Capital Fund Management.
    2. Miffre, Joelle & Rallis, Georgios, 2007. "Momentum strategies in commodity futures markets," Journal of Banking & Finance, Elsevier, vol. 31(6), pages 1863-1886, June.
    3. David M. Cutler & James M. Poterba & Lawrence H. Summers, 1991. "Speculative Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 529-546.
    4. Farmer, J. Doyne & Joshi, Shareen, 2002. "The price dynamics of common trading strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 149-171, October.
    5. Moskowitz, Tobias J. & Ooi, Yao Hua & Pedersen, Lasse Heje, 2012. "Time series momentum," Journal of Financial Economics, Elsevier, vol. 104(2), pages 228-250.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schmidhuber, Christof, 2021. "Trends, reversion, and critical phenomena in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    2. Schmidhuber, Christof, 2022. "Financial markets and the phase transition between water and steam," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    3. He, Xue-Zhong & Li, Kai, 2015. "Profitability of time series momentum," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 140-157.
    4. Sandrine Jacob Leal, 2015. "Fundamentalists, chartists and asset pricing anomalies," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1837-1850, November.
    5. Christof Schmidhuber, 2020. "Trends, Reversion, and Critical Phenomena in Financial Markets," Papers 2006.07847, arXiv.org, revised Dec 2020.
    6. Sandrine Jacob Leal, 2015. "Fundamentalists, Chartists and Asset pricing anomalies," Post-Print hal-01508002, HAL.
    7. Sandrine Jacob Leal, 2013. "Momentum effect in individual stocks and heterogeneous beliefs among fundamentalists," Economics Bulletin, AccessEcon, vol. 33(4), pages 3102-3116.
    8. He, Xue-Zhong & Li, Kai & Santi, Caterina & Shi, Lei, 2022. "Social interaction, volatility clustering, and momentum," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 125-149.
    9. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, December.
    10. Bianchi, Robert J. & Drew, Michael E. & Fan, John Hua, 2016. "Commodities momentum: A behavioral perspective," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 133-150.
    11. Miffre, Joëlle, 2016. "Long-short commodity investing: A review of the literature," Journal of Commodity Markets, Elsevier, vol. 1(1), pages 3-13.
    12. Yasuhiro Iwanaga & Ryuta Sakemoto, 2023. "Commodity momentum decomposition," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(2), pages 198-216, February.
    13. Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
    14. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2020. "Does sophistication of the weighting scheme enhance the performance of long-short commodity portfolios?," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 164-180.
    15. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    16. Giulio Bottazzi & Pietro Dindo & Daniele Giachini, 2019. "Momentum and reversal in financial markets with persistent heterogeneity," Annals of Finance, Springer, vol. 15(4), pages 455-487, December.
    17. Bacchetta, Philippe & Davenport, Margaret & van Wincoop, Eric, 2022. "Can sticky portfolios explain international capital flows and asset prices?," Journal of International Economics, Elsevier, vol. 136(C).
    18. Benjamin R. Auer, 2021. "Have trend-following signals in commodity futures markets become less reliable in recent years?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 533-553, December.
    19. Guillaume Coqueret, 2016. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02088097, HAL.
    20. Sina Ehsani & Juhani T. Linnainmaa, 2019. "Factor Momentum and the Momentum Factor," NBER Working Papers 25551, National Bureau of Economic Research, Inc.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2107.03857. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.