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PLOS ONE  2011 

Investment Strategies Used as Spectroscopy of Financial Markets Reveal New Stylized Facts

DOI: 10.1371/journal.pone.0024391

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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 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.

References

[1]  Dobzhansky T (1973) Nothing in biology makes sense except in the light of evolution. Amer Bio Teacher 35: 125–129.
[2]  Haldane AG, May RM (2011) Systemic risk in banking ecosystems. Nature 469: 351–355.
[3]  Johnson N (2011) Proposing policy by analogy is risky. Nature 469: 302.
[4]  Lux T (2011) Network theory is sorely required. Nature 469: 303.
[5]  Biais B, Hillion P, Spatt C (1995) An empirical analysis of the limit order book and the order flow in the Paris Bourse. J Financ 50: 1655–1689.
[6]  Mantegna RN, Stanley HE (2000) An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge: Cambridge University Press.
[7]  Cont R (2001) Empirical properties of asset returns: Stylized facts and statistical issues. Quant Financ 1: 223–236.
[8]  Sornette D, Woordard R (2010) Financial Bubbles, Real Estate bubbles, Derivative Bubbles, and the Financial and Economic Crisis. In: Takayasu H, Takayasu M, Watanabe T, editors. Econophysics Approaches to Large-Scale Business Data and Financial Crisis. Tokyo: Springer. pp. 101–148.
[9]  Bernanke BS (2004) The Great Moderation. Remarks by Governor Ben S. Bernanke at the meetings of the Eastern Economic Association, Washington, DC, February 20, 2004.
[10]  LeBaron B, Arthur WB, Palmer R (1999) Time series properties of an artificial stock market. J Econ Dyn Control 23: 1487–1516.
[11]  Hommes CH (2001) Financial markets as nonlinear adaptive evolutionary systems. Quant Financ 1: 149–167.
[12]  Hommes CH (2002) Modeling the stylized facts in finance through simple nonlinear adaptive systems. Proc Natl Acad Sci USA 99: 7221–7228.
[13]  Farmer JD (2002) Market force, ecology and evolution. Industrial and Corporate Change 11: 895–953.
[14]  Ehrentreich N (2006) Technical trading in the Santa Fe Institute Artificial Stock Market revisited. J Econ Behav Org 61: 599–616.
[15]  Hommes C, Wagener F (2009) Complex Evolutionary Systems in Behavioral Finance. Amsterdam, The Netherlands: North-Holland, Elsevier, Inc. Chapter 4 of Handbook of Financial Markets: Dynamics and Evolution. pp. 217–276.
[16]  Lo AW (2004) The adaptive markets hypothesis. J Portfolio Management 30: 15–29.
[17]  Gode DK, Sunder S (1993) Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. J Polit Econ 101: 119–137.
[18]  Othman A (2008) Zero-Intelligence Agents in Prediction Markets. In: Padgham M, Parsons Parkes, editors. pp. 879–886. Proc. of 7th Int. Conf. on Autonomous Agents and Multiagent Systems. Estoril, Portugal: AAMAS 2008.
[19]  Farmer JD, Patelli P, Zovko II (2005) The predictive power of zero intelligence in financial markets. Proc Natl Acad Sci USA 102: 2254–2259.
[20]  Malkiel BG (2011) A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing. New York: W. W. Norton & Company.
[21]  Barras L, Scaillet O, Wermers R (2010) False discoveries in mutual fund performance: Measuring luck in estimated alphas. J Financ 65: 179–216.
[22]  Fama E, French K (2010) Luck versus skill in the cross-section of mutual fund returns. J Financ 65: 1915–1947.
[23]  Kosowski R, Timmermann A, Wermers R, White H (2006) Can mutual fund “stars” really pick stocks? New evidence from a bootstrap analysis. J Financ 61: 2551–2595.
[24]  Satinover JB, Sornette D (2007) Illusion of control in time-horizon minority and parrondo games. Eur Phys J B 60: 369–384.
[25]  Gu GF, Chen W, Zhou WX (2007) Quantifying bid-ask spreads in the Chinese stock market using limit-order book data: Intraday pattern, probability distribution, long memory, and multifractal nature. Eur Phys J B 57: 81–87.
[26]  DeMiguel V, Garlappi L, Uppal R (2009) Optimal versus naive diversification: How enefficient is the 1/N portfolio strategy. Rev Financ Stud 53: 1915–1953.
[27]  Parrondo JMR (1996) Efficiency of Brownian motors. Workshop of the EEC HC&M Network on Complexity and Chaos.
[28]  Harmer GP, Abbott D (1999) Parrondo's paradox. Statist Sci 14: 206–213.
[29]  Harmer GP, Abbott D (1999) Losing strategies can win by Parrondo's Paradox. Nature 402: 864.
[30]  Harmer GP, Abbott D, Taylor PG, Parrondo JMR (2000) Parrondo's paradoxical games and the discrete Brownian ratchet. Unsolved Problems of Noise and Fluctuations 511: 189–200.
[31]  Satinover JB, Sornette D (2007) Illusion of control in a Brownian game. Physica A 386: 339–344.
[32]  Satinover JB, Sornette D (2009) Illusory versus genuine control in agent-based games. Eur Phys J B 67: 357–367.
[33]  Barber BM, Odean T, Zhu N (2009) Do retail trades move markets? Rev Financ Stud 22: 151–186.
[34]  Torngren G, Montgomery H (2004) Worse than chance? Performance and confidence among professionals and laypeople in the stock market. J Behav Financ 5: 148–153.
[35]  Ljungqvist A, Malloy C, Marston F (2009) Rewriting history. J Financ 64: 1935–1960.
[36]  Daniel G, Sornette D, Woehrmann P (2009) Look-ahead benchmark bias in portfolio performance evaluation. J Portfolio Management 36: 121–130.
[37]  Odean T (1998) Volume, volatility, price and profit when all traders are above average. J Financ 53: 1887–1934.
[38]  Gervais S, Odean T (2001) Learning to be overconfident. Rev Financ Stud 14: 1–27.
[39]  Statman M, Thorley S, Vorkink K (2006) Investor overconfidence and trading volume. Rev Financ Stud 19: 1531–1565.
[40]  Glaser M, Weber M (2007) Overconfidence and trading volume. Geneva Risk Insur Rev 32: 1–36.
[41]  Glaser M, Weber M (2009) Which past returns affect trading volume? J Financ Markets 12: 1–31.
[42]  Deaves R, Lüders E, Luo GY (2009) An experimental test of the impact of overconfidence and gender on trading activity. Rev Financ 13: 555–575.
[43]  Odean T (1999) Do investors trade too much? Amer Econ Rev 89: 1279–1298.
[44]  Barber BM, Odean T (2000) Trading is hazardous to your wealth: The common stock investment performance of individual investors. J Financ 55: 773–806.
[45]  Barber BM, Lee Y, Liu Y, Odean T (2009) Just how much do individual investors lose by trading? Rev Financ Stud 22: 609–632.
[46]  MacKenzie D (2008) An Engine, Not a Camera: How Financial Models Shape Markets, volume 1. London: The MIT Press.

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