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Strategies used as spectroscopy of financial markets reveal new stylized facts

Author

Listed:
  • Wei-Xing Zhou

    (ECUST)

  • Guo-Hua Mu

    (ECUST)

  • Wei Chen

    (SZSE)

  • Didier Sornette

    (ETH Zurich)

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 investors to random strategies with respect to timing that share otherwise all other characteristics. We find that more trading results in smaller net return due to trading frictions. 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 investors. 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 & Wei Chen & Didier Sornette, 2011. "Strategies used as spectroscopy of financial markets reveal new stylized facts," Papers 1104.3616, arXiv.org.
  • Handle: RePEc:arx:papers:1104.3616
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    File URL: http://arxiv.org/pdf/1104.3616
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    References listed on IDEAS

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    1. Gervais, Simon & Odean, Terrance, 2001. "Learning to be Overconfident," Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 1-27.
    2. 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.
    3. 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.
    4. 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.
    5. Alexander Ljungqvist & Christopher Malloy & Felicia Marston, 2009. "Rewriting History," Journal of Finance, American Finance Association, vol. 64(4), pages 1935-1960, August.
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    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.

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