IDEAS home Printed from https://ideas.repec.org/p/stz/wpaper/eth-rc-11-005.html
   My bibliography  Save this paper

Strategies used as Spectroscopy of Financial Markets Reveal New Stylized Facts

Author

Listed:
  • Wei-Xing Zhou
  • Guo-Hua Mu
  • Si-Wei Chen
  • Didier Sornette

Abstract

We propose a new set of stylized facts quantifying the structure of financial markets. The key idea is to study the combined structure of both investment strategies and prices in order to open a qualitatively new level of understanding of financial and economic markets. We study the detailed order flow on the Shenzhen Stock Exchange of China for the whole year of 2003. This enormous dataset allows us to compare (i) a closed national market (A-shares) with an international market (B-shares), (ii) individuals and institutions and (iii) real traders to random strategies with respect to timing that share otherwise all other characteristics. We find in general that more trading results in smaller net return due to trading frictions, with the exception that the net return is independent of the trading frequency for A-share individual traders. We unveiled quantitative power laws with non-trivial exponents, that quantify the deterioration of performance with frequency and with holding period of the strategies used by traders. Random strategies are found to perform much better than real ones, both for winners and losers. Surprising large arbitrage opportunities exist, especially when using zero-intelligence strategies. This is a diagnostic of possible inefficiencies of these financial markets.

Suggested Citation

  • Wei-Xing Zhou & Guo-Hua Mu & Si-Wei Chen & Didier Sornette, "undated". "Strategies used as Spectroscopy of Financial Markets Reveal New Stylized Facts," Working Papers ETH-RC-11-005, ETH Zurich, Chair of Systems Design.
  • Handle: RePEc:stz:wpaper:eth-rc-11-005
    as

    Download full text from publisher

    File URL: ftp://web.sg.ethz.ch/RePEc/stz/wpaper/pdf/ETH-RC-11-005.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Gao-Feng Gu & Wei Chen & Wei-Xing Zhou, 2006. "Quantifying bid-ask spreads in the Chinese stock market using limit-order book data: Intraday pattern, probability distribution, long memory, and multifractal nature," Papers physics/0701017, arXiv.org, revised Mar 2007.
    2. Gervais, Simon & Odean, Terrance, 2001. "Learning to be Overconfident," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 1-27.
    3. Donald MacKenzie, 2008. "An Engine, Not a Camera: How Financial Models Shape Markets," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262633671, December.
    4. J. Doyne Farmer, 2002. "Market force, ecology and evolution," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(5), pages 895-953, November.
    5. Terrance Odean., 1996. "Volume, Volatility, Price and Profit When All Trader Are Above Average," Research Program in Finance Working Papers RPF-266, University of California at Berkeley.
    6. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    7. C. H. Hommes, 2001. "Financial markets as nonlinear adaptive evolutionary systems," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 149-167.
    8. Didier Sornette & Wei-Xing Zhou, 2005. "Non-parametric determination of real-time lag structure between two time series: the 'optimal thermal causal path' method," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 577-591.
    9. Biais, Bruno & Hillion, Pierre & Spatt, Chester, 1995. "An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse," Journal of Finance, American Finance Association, vol. 50(5), pages 1655-1689, December.
    10. Brad M. Barber & Terrance Odean & Ning Zhu, 2009. "Do Retail Trades Move Markets?," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 151-186, January.
    11. Glaser, Markus & Weber, Martin, 2009. "Which past returns affect trading volume?," Journal of Financial Markets, Elsevier, vol. 12(1), pages 1-31, February.
    12. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    13. J. B. Satinover & D. Sornette, 2007. "”Illusion of control” in Time-Horizon Minority and Parrondo Games," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(3), pages 369-384, December.
    14. Satinover, J.B. & Sornette, D., 2007. "Illusion of control in a Brownian game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 339-344.
    15. Brad M. Barber & Terrance Odean, 2000. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors," Journal of Finance, American Finance Association, vol. 55(2), pages 773-806, April.
    16. Markus Glaser & Martin Weber, 2007. "Overconfidence and trading volume," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 32(1), pages 1-36, June.
    17. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    18. Andrew G. Haldane & Robert M. May, 2011. "Systemic risk in banking ecosystems," Nature, Nature, vol. 469(7330), pages 351-355, January.
    19. G.-F. Gu & W. Chen & W.-X. Zhou, 2007. "Quantifying bid-ask spreads in the Chinese stock market using limit-order book data," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(1), pages 81-87, May.
    20. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    21. Ehrentreich, Norman, 2006. "Technical trading in the Santa Fe Institute Artificial Stock Market revisited," Journal of Economic Behavior & Organization, Elsevier, vol. 61(4), pages 599-616, December.
    22. Meir Statman & Steven Thorley & Keith Vorkink, 2006. "Investor Overconfidence and Trading Volume," The Review of Financial Studies, Society for Financial Studies, vol. 19(4), pages 1531-1565.
    23. Hommes, C.H. & Wagener, F.O.O., 2008. "Complex evolutionary systems in behavioral finance," CeNDEF Working Papers 08-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    24. Robert Kosowski & Allan Timmermann & Russ Wermers & Hal White, 2006. "Can Mutual Fund “Stars” Really Pick Stocks? New Evidence from a Bootstrap Analysis," Journal of Finance, American Finance Association, vol. 61(6), pages 2551-2595, December.
    25. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    26. Alexander Ljungqvist & Christopher Malloy & Felicia Marston, 2009. "Rewriting History," Journal of Finance, American Finance Association, vol. 64(4), pages 1935-1960, August.
    27. Richard Deaves & Erik Lüders & Guo Ying Luo, 2009. "An Experimental Test of the Impact of Overconfidence and Gender on Trading Activity," Review of Finance, European Finance Association, vol. 13(3), pages 555-575.
    28. Terrance Odean, 1999. "Do Investors Trade Too Much?," American Economic Review, American Economic Association, vol. 89(5), pages 1279-1298, December.
    29. Terrance Odean, 1998. "Volume, Volatility, Price, and Profit When All Traders Are Above Average," Journal of Finance, American Finance Association, vol. 53(6), pages 1887-1934, December.
    30. J. B. Satinover & D. Sornette, 2009. "Illusory versus genuine control in agent-based games," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 357-367, February.
    31. Didier Sornette & Ryan Woodard, 2009. "Financial Bubbles, Real Estate bubbles, Derivative Bubbles, and the Financial and Economic Crisis," Papers 0905.0220, arXiv.org.
    32. Eugene F. Fama & Kenneth R. French, 2010. "Luck versus Skill in the Cross‐Section of Mutual Fund Returns," Journal of Finance, American Finance Association, vol. 65(5), pages 1915-1947, October.
    33. Brad M. Barber & Yi-Tsung Lee & Yu-Jane Liu & Terrance Odean, 2009. "Just How Much Do Individual Investors Lose by Trading?," The Review of Financial Studies, Society for Financial Studies, vol. 22(2), pages 609-632, February.
    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. J. Wiesinger & D. Sornette & J. Satinover, 2013. "Reverse Engineering Financial Markets with Majority and Minority Games Using Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 475-492, April.
    2. Yan Li & Bo Zheng & Ting-Ting Chen & Xiong-Fei Jiang, 2017. "Fluctuation-driven price dynamics and investment strategies," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-15, December.
    3. Jun-Jie Chen & Bo Zheng & Lei Tan, 2013. "Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-11, November.
    4. Mario A Bertella & Felipe R Pires & Henio H A Rego & Jonathas N Silva & Irena Vodenska & H Eugene Stanley, 2017. "Confidence and self-attribution bias in an artificial stock market," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-20, February.
    5. Jun-jie Chen & Bo Zheng & Lei Tan, 2014. "Agent-based model with asymmetric trading and herding for complex financial systems," Papers 1407.5258, arXiv.org.
    6. Kevin Primicerio & Damien Challet, 2018. "Large large-trader activity weakens the long memory of limit order markets," Papers 1803.08390, arXiv.org.
    7. Lucas Fievet & Didier Sornette, 2018. "Calibrating emergent phenomena in stock markets with agent based models," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.

    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. Barber, Brad M. & Odean, Terrance, 2013. "The Behavior of Individual Investors," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1533-1570, Elsevier.
    2. Mushinada, Venkata Narasimha Chary, 2020. "Are individual investors irrational or adaptive to market dynamics?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    3. Forman, John & Horton, Joanne, 2019. "Overconfidence, position size, and the link to performance," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 291-309.
    4. Bregu, Klajdi, 2020. "Overconfidence and (Over)Trading: The Effect of Feedback on Trading Behavior," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 88(C).
    5. Oliver Gloede & Lukas Menkhoff, 2014. "Financial Professionals' Overconfidence: Is It Experience, Function, or Attitude?," European Financial Management, European Financial Management Association, vol. 20(2), pages 236-269, March.
    6. Helen X. H. Bao & Steven Haotong Li, 2016. "Overconfidence And Real Estate Research: A Survey Of The Literature," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 61(04), pages 1-24, September.
    7. Zhang, Xiaotao & Liang, Junpeng & He, Feng, 2019. "Private information advantage or overconfidence? Performance of intraday arbitrage speculators in the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    8. Merkle, Christoph, 2018. "The curious case of negative volatility," Journal of Financial Markets, Elsevier, vol. 40(C), pages 92-108.
    9. Margarida Abreu, 2017. "How Biased is the Behavior of the Individual Investor in Warrants?," Working Papers REM 2017/07, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    10. Merkle, Christoph, 2017. "Financial overconfidence over time: Foresight, hindsight, and insight of investors," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 68-87.
    11. Phan, Thuy Chung & Rieger, Marc Oliver & Wang, Mei, 2018. "What leads to overtrading and under-diversification? Survey evidence from retail investors in an emerging market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 19(C), pages 39-55.
    12. Kleine, Jens & Wagner, Niklas & Weller, Tim, 2016. "Openness endangers your wealth: Noise trading and the big five," Finance Research Letters, Elsevier, vol. 16(C), pages 239-247.
    13. Margaria Abreu, 2017. "HOW Biased is the Behavior of the Individual Investor in Warrants?," Working Papers Department of Economics 2017/18, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    14. Chou, Robin K. & Wang, Yun-Yi, 2011. "A test of the different implications of the overconfidence and disposition hypotheses," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 2037-2046, August.
    15. Abreu, Margarida & Mendes, Victor, 2012. "Information, overconfidence and trading: Do the sources of information matter?," Journal of Economic Psychology, Elsevier, vol. 33(4), pages 868-881.
    16. Helen X. H. Bao & Steven Haotong Li, 2020. "Investor Overconfidence and Trading Activity in the Asia Pacific REIT Markets," JRFM, MDPI, vol. 13(10), pages 1-21, September.
    17. Abreu, Margarida, 2019. "How biased is the behavior of the individual investor in warrants?," Research in International Business and Finance, Elsevier, vol. 47(C), pages 139-149.
    18. Kenneth Yung & Yen-Chih Liu, 2009. "Implications of futures trading volume: Hedgers versus speculators," Journal of Asset Management, Palgrave Macmillan, vol. 10(5), pages 318-337, December.
    19. Piet Eichholtz & Erkan Yönder, 2015. "CEO Overconfidence, REIT Investment Activity and Performance," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(1), pages 139-162, March.
    20. Michailova, Julija, 2010. "Development of the overconfidence measurement instrument for the economic experiment," MPRA Paper 34799, University Library of Munich, Germany, revised Nov 2011.

    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:stz:wpaper:eth-rc-11-005. 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: Claudio J. Tessone (email available below). General contact details of provider: https://edirc.repec.org/data/dmethch.html .

    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.