IDEAS home Printed from https://ideas.repec.org/a/jfr/ijfr11/v8y2017i3p1-26.html
   My bibliography  Save this article

Mandelbrot Market-Model and Momentum

Author

Listed:
  • Wilhelm Berghorn
  • Sascha Otto

Abstract

Mandelbrot was one of the first who criticized the oversimplifications in finance modeling. In his view, markets have long-term memory, were fractal and thus much wilder than classical theory suggests. Recently, we were able to show that the scaling behaviour of trends, as defined by a specific trend decomposition using wavelets, are causing the momentum effect. In this work, we will show that this effect can be modeled by fractal trends. The so-called Mandelbrot Market-Model shows that markets are wilder compared with classical models. In conclusion, we derive what Mandelbrot always knew: There are no efficient markets.

Suggested Citation

  • Wilhelm Berghorn & Sascha Otto, 2017. "Mandelbrot Market-Model and Momentum," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 8(3), pages 1-26, July.
    • Handle: RePEc:jfr:ijfr11:v:8:y:2017:i:3:p:1-26
    DOI: 10.5430/ijfr.v8n3p1
    as

    Download full text from publisher

    File URL: http://www.sciedu.ca/journal/index.php/ijfr/article/view/11698/7189
    Download Restriction: no
    File URL: http://www.sciedu.ca/journal/index.php/ijfr/article/view/11698
    Download Restriction: no
    File URL: https://libkey.io/10.5430/ijfr.v8n3p1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Eugene F. Fama & Kenneth R. French, 2008. "Dissecting Anomalies," Journal of Finance, American Finance Association, vol. 63(4), pages 1653-1678, August.
    2. Fama, Eugene F. & French, Kenneth R., 2012. "Size, value, and momentum in international stock returns," Journal of Financial Economics, Elsevier, vol. 105(3), pages 457-472.
    3. B. B. Mandelbrot, 2001. "Scaling in financial prices: III. Cartoon Brownian motions in multifractal time," Quantitative Finance, Taylor & Francis Journals, vol. 1(4), pages 427-440.
    4. B. B. Mandelbrot, 2001. "Scaling in financial prices: I. Tails and dependence," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 113-123.
    5. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    7. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    8. B. B. Mandelbrot, 2001. "Scaling in financial prices: II. Multifractals and the star equation," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 124-130.
    9. B. B. Mandelbrot, 2001. "Scaling in financial prices: IV. Multifractal concentration," Quantitative Finance, Taylor & Francis Journals, vol. 1(6), pages 641-649.
    10. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vogl, Markus, 2023. "Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framewo," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

    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. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
    2. Sutthisit Jamdee & Cornelis A. Los, 2005. "Multifractal Modeling of the US Treasury Term Structure and Fed Funds Rate," Finance 0502021, University Library of Munich, Germany.
    3. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    4. Benoit B. Mandelbrot, 2005. "Parallel cartoons of fractal models of finance," Annals of Finance, Springer, vol. 1(2), pages 179-192, October.
    5. M. A. H. Dempster, 2011. "Benoit B. Mandelbrot (1924-2010): a father of Quantitative Finance," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 155-156.
    6. Wilhelm Berghorn & Martin T. Schulz & Markus Vogl & Sascha Otto, 2021. "Trend Momentum II: Driving Forces of Low Volatility and Momentum," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 300-319, May.
    7. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, September.
    8. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    9. Bartram, Söhnke M. & Grinblatt, Mark, 2021. "Global market inefficiencies," Journal of Financial Economics, Elsevier, vol. 139(1), pages 234-259.
    10. Shi, Leilei & Wang, Binghong & Guo, Xinshuai & Li, Honggang, 2021. "A price dynamic equilibrium model with trading volume weights based on a price-volume probability wave differential equation," International Review of Financial Analysis, Elsevier, vol. 74(C).
    11. Arthur, Bruno R. & Katchova, Ani L., 2013. "Uncertainty and Value Premium: Evidence from the U.S. Agriculture Industry," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 143198, Southern Agricultural Economics Association.
    12. Ailie Charteris & Mukashema Rwishema & Tafadzwa-Hidah Chidede, 2018. "Asset Pricing and Momentum: A South African Perspective," Journal of African Business, Taylor & Francis Journals, vol. 19(1), pages 62-85, January.
    13. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
    14. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    15. Sebastien Valeyre, 2020. "Refined model of the covariance/correlation matrix between securities," Papers 2001.08911, arXiv.org.
    16. Jean de Carufel & Martin Brooks & Michael Stieber & Paul Britton, 2017. "A Topological Approach to Scaling in Financial Data," Papers 1710.08860, arXiv.org.
    17. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    18. Chris Heyde, 2009. "Scaling issues for risky asset modelling," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 593-603, July.
    19. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    20. Patton, Andrew J. & Weller, Brian M., 2020. "What you see is not what you get: The costs of trading market anomalies," Journal of Financial Economics, Elsevier, vol. 137(2), pages 515-549.

    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:jfr:ijfr11:v:8:y:2017:i:3:p:1-26. 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: Gina Perry (email available below). General contact details of provider: http://ijfr.sciedupress.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.