[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i6p860-d766680.html
   My bibliography  Save this article

Econophysics Techniques and Their Applications on the Stock Market

Author

Listed:
  • Florin Turcaș

    (ANEVAR, 011158 Bucharest, Romania)

  • Florin Cornel Dumiter

    (Economics and Technical Department, “Vasile Goldiș” Western University of Arad, 310025 Arad, Romania)

  • Marius Boiță

    (Economics and Technical Department, “Vasile Goldiș” Western University of Arad, 310025 Arad, Romania)

Abstract
Exact sciences have achieved many results, validated in practice. Although their application in economics is difficult due to the human factor involved, the lack of conservation laws, and experimental difficulties, it must be highlighted that the consistent bibliography gathered in recent years in this field encourages the econophysics approach. The objective of this article is to validate and/or define a few stock strategies, based on known results from mathematics, physics, and chemistry. The scope of this research demonstrates that statistics (in portfolio theory), geometry (in technical analysis), or financial mathematics can be used in the capital market. Many of the exact science results corresponded to strategies applicable to investors. Unlike the material world, financial markets have additional components that must be considered: human psychology, sociology at the firm level, and behavioral unpredictability. The findings obtained in this research enable the enormous vastness of the exact science results that can be a fertile source for new investment strategies. This article concludes that in order for mathematical theories to be applied to the stock market, it is essential that the start-up conditions (initial assumptions) are validated in the market.

Suggested Citation

  • Florin Turcaș & Florin Cornel Dumiter & Marius Boiță, 2022. "Econophysics Techniques and Their Applications on the Stock Market," Mathematics, MDPI, vol. 10(6), pages 1-25, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:860-:d:766680
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/6/860/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/6/860/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giovani L. Vasconcelos, 2004. "A Guided Walk Down Wall Street: an Introduction to Econophysics," Papers cond-mat/0408143, arXiv.org.
    2. Lei Ruan, 2018. "Research on Sustainable Development of the Stock Market Based on VIX Index," Sustainability, MDPI, vol. 10(11), pages 1-12, November.
    3. Adnen Ben Nasr & Juncal Cunado & Rıza Demirer & Rangan Gupta, 2018. "Country Risk Ratings and Stock Market Returns in Brazil, Russia, India, and China (BRICS) Countries: A Nonlinear Dynamic Approach," Risks, MDPI, vol. 6(3), pages 1-22, September.
    4. Jonathan Blackledge & Derek Kearney & Marc Lamphiere & Raja Rani & Paddy Walsh, 2019. "Econophysics and Fractional Calculus: Einstein’s Evolution Equation, the Fractal Market Hypothesis, Trend Analysis and Future Price Prediction," Mathematics, MDPI, vol. 7(11), pages 1-57, November.
    5. Jewson,Stephen & Brix,Anders, 2005. "Weather Derivative Valuation," Cambridge Books, Cambridge University Press, number 9780521843713, September.
    6. Roehner,Bertrand M., 2002. "Patterns of Speculation," Cambridge Books, Cambridge University Press, number 9780521802635, September.
    7. Rafał Dreżewski & Grzegorz Dziuban & Karol Pająk, 2018. "The Bio-Inspired Optimization of Trading Strategies and Its Impact on the Efficient Market Hypothesis and Sustainable Development Strategies," Sustainability, MDPI, vol. 10(5), pages 1-45, May.
    8. Muhammad Aamir & Syed Zulfiqar Ali Shah, 2018. "Determinants of Stock Market Co-Movements between Pakistan and Asian Emerging Economies," JRFM, MDPI, vol. 11(3), pages 1-14, June.
    9. Kakarot-Handtke, Egmont, 2013. "Toolism! A Critique of Econophysics," MPRA Paper 46630, University Library of Munich, Germany.
    10. Tolga Ulusoy, 2017. "Price Fluctuations in Econophysics," Contributions to Economics, in: Ümit Hacioğlu & Hasan Dinçer (ed.), Global Financial Crisis and Its Ramifications on Capital Markets, pages 459-474, Springer.
    11. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: II. Agent-based models," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1013-1041.
    12. Janda, K & Rausser, G & Svárovská, B, 2014. "Can investment in microfinance funds improve risk-return characteristics of a portfolio?," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt61k33595, Department of Agricultural & Resource Economics, UC Berkeley.
    13. Biao Li & Kekun Wu, 2017. "The Price of Environmental Sustainability: Empirical Evidence from Stock Market Performance in China," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    14. Emanuele Rossi & Gianfranco Forte, 2016. "Assessing Relative Valuation in Equity Markets," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-56335-4, October.
    15. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: II. Agent-based models," Post-Print hal-00621059, HAL.
    16. Richmond, Peter & Mimkes, Jurgen & Hutzler, Stefan, 2013. "Econophysics and Physical Economics," OUP Catalogue, Oxford University Press, number 9780199674701.
    17. Bodo Herzog & Sufyan Osamah, 2019. "Reverse Engineering of Option Pricing: An AI Application," IJFS, MDPI, vol. 7(4), pages 1-12, November.
    18. Prodromos E. Tsinaslanidis & Achilleas D. Zapranis, 2016. "Technical Analysis for Algorithmic Pattern Recognition," Springer Books, Springer, number 978-3-319-23636-0, December.
    19. John Fry & Andrew Brint, 2017. "Bubbles, Blind-Spots and Brexit," Risks, MDPI, vol. 5(3), pages 1-15, July.
    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. Poitras, Geoffrey, 2018. "The pre-history of econophysics and the history of economics: Boltzmann versus the marginalists," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 89-98.
    2. Nikolaos Th. Chatzarakis, 2021. "Revisiting the role and consequences of Econophysics from a Marxian perspective," Bulletin of Political Economy, Bulletin of Political Economy, vol. 15(1), pages 45-68, June.
    3. Restocchi, Valerio & McGroarty, Frank & Gerding, Enrico, 2019. "Statistical properties of volume and calendar effects in prediction markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1150-1160.
    4. Christoph J. Borner & Ingo Hoffmann & John H. Stiebel, 2024. "A closer look at the chemical potential of an ideal agent system," Papers 2401.09233, arXiv.org.
    5. Kiran Sharma & Subhradeep Das & Anirban Chakraborti, 2017. "Global Income Inequality and Savings: A Data Science Perspective," Papers 1801.00253, arXiv.org, revised Aug 2018.
    6. Ivan Jericevich & Murray McKechnie & Tim Gebbie, 2021. "Calibrating an adaptive Farmer-Joshi agent-based model for financial markets," Papers 2104.09863, arXiv.org.
    7. Ban Zheng & François Roueff & Frédéric Abergel, 2014. "Ergodicity and scaling limit of a constrained multivariate Hawkes process," Post-Print hal-00777941, HAL.
    8. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of a point-process market-model with a matching engine," Papers 2105.02211, arXiv.org, revised Aug 2021.
    9. Seemann, Lars & Hua, Jia-Chen & McCauley, Joseph L. & Gunaratne, Gemunu H., 2012. "Ensemble vs. time averages in financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6024-6032.
    10. A. Lykov & S. Muzychka & K. Vaninsky, 2012. "Investor's sentiment in multi-agent model of the continuous double auction," Papers 1208.3083, arXiv.org, revised Feb 2016.
    11. A. O. Glekin & A. Lykov & K. L. Vaninsky, 2014. "On Simulation of Various Effects in Consolidated Order Book," Papers 1402.4150, arXiv.org.
    12. Hong Guo & Jianwu Lin & Fanlin Huang, 2023. "Market Making with Deep Reinforcement Learning from Limit Order Books," Papers 2305.15821, arXiv.org.
    13. Fei Cao & Sebastien Motsch, 2021. "Derivation of wealth distributions from biased exchange of money," Papers 2105.07341, arXiv.org.
    14. Pierre Gosselin & Aïleen Lotz & Marc Wambst, 2020. "A path integral approach to business cycle models with large number of agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 899-942, October.
    15. Dias, Thiago & Gonçalves, Sebastián, 2024. "Effectiveness of wealth-based vs exchange-based tax systems in reducing inequality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    16. Zhu, Lirong & Chen, Jiawei & Di, Zengru & Chen, Liujun & Liu, Yan & Stanley, H. Eugene, 2017. "The mechanisms of labor division from the perspective of individual optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 488(C), pages 112-120.
    17. Moura, N.J. & Ribeiro, Marcelo B., 2013. "Testing the Goodwin growth-cycle macroeconomic dynamics in Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2088-2103.
    18. Pierre Gosselin & Aïleen Lotz & Marc Wambst, 2019. "A Statistical Field Approach to Capital Accumulation," Working Papers hal-02280634, HAL.
    19. Stein, Julian Alexander Cornelius & Braun, Dieter, 2019. "Stability of a time-homogeneous system of money and antimoney in an agent-based random economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 232-249.
    20. 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.

    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:gam:jmathe:v:10:y:2022:i:6:p:860-:d:766680. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.