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Strategies used as spectroscopy of financial markets reveal new stylized facts

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  • Wei-Xing Zhou

    (ECUST)

  • Guo-Hua Mu

    (ECUST)

  • Wei Chen

    (SZSE)

  • Didier Sornette

    (ETH Zurich)

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    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.

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    File URL: http://arxiv.org/pdf/1104.3616
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1104.3616.

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    Date of creation: Apr 2011
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    Publication status: Published in PLoS ONE 6 (9), e24391 (2011)
    Handle: RePEc:arx:papers:1104.3616

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    Web page: http://arxiv.org/

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    1. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, American Finance Association, vol. 65(1), pages 179-216, 02.
    2. Cars Hommes & Florian Wagener, 2008. "Complex Evolutionary Systems in Behavioral Finance," Tinbergen Institute Discussion Papers, Tinbergen Institute 08-054/1, Tinbergen Institute.
    3. Simon Gervais & Terrance Odean, . "Learning To Be Overconfident," Rodney L. White Center for Financial Research Working Papers, Wharton School Rodney L. White Center for Financial Research 05-97, Wharton School Rodney L. White Center for Financial Research.
    4. 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, University of Chicago Press, vol. 101(1), pages 119-37, February.
    5. Alexander Ljungqvist & Christopher Malloy & Felicia Marston, 2009. "Rewriting History," Journal of Finance, American Finance Association, American Finance Association, vol. 64(4), pages 1935-1960, 08.
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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, Society for Computational Economics, Society for Computational Economics, vol. 41(4), pages 475-492, April.

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