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The trend is not your friend! Why empirical timing success is determined by the underlying's price characteristics and market efficiency is irrelevant

  • Scholz, Peter
  • Walther, Ursula
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    The often reported empirical success of trend-following technical timing strategies remains to be puzzling. In previous academic research, many authors admit some prediction power but struggle to substantiate their findings by referring vaguely to insufficient market effciency or unknown hidden patterns in asset price processes. We claim that empirical timing success is possible even in perfectly efficient markets but does not indicate prediction power. We prove this by systematically tracing back timing success to the statistical characteristics of the underlying asset price time series, which is modeled by standard stochastic processes. Five major impact factors are studied: return autocorrelation, trend, volatility and its clustering as well as the degree of market efficiency. We use trading rules based on different intervals of the simple moving average (SMA) as an example. These strategies are applied to simulated asset price data to allow for systematic parameter variations. Subsequently, we test the same strategies on real market data using non-parametric historical simulations and compare the results. Evaluation is done by an extensive selection of statistical-, return-, risk-, and performance figures calculated from the simulated return distributions.

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    File URL: http://econstor.eu/bitstream/10419/55525/1/665568843.pdf
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    Paper provided by Frankfurt School of Finance and Management, Centre for Practical Quantitative Finance (CPQF) in its series CPQF Working Paper Series with number 29.

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    Date of creation: 2011
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    Handle: RePEc:zbw:cpqfwp:29
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    1. Neely, Christopher J., 2003. "Risk-adjusted, ex ante, optimal technical trading rules in equity markets," International Review of Economics & Finance, Elsevier, vol. 12(1), pages 69-87.
    2. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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    5. Delroy Hunter, 1998. "The performance of filter rules on the Jamaican Stock Exchange," Applied Economics Letters, Taylor & Francis Journals, vol. 5(5), pages 297-300.
    6. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-64, December.
    7. Sweeney, Richard J., 1988. "Some New Filter Rule Tests: Methods and Results," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 23(03), pages 285-300, September.
    8. Annaert, Jan & Osselaer, Sofieke Van & Verstraete, Bert, 2009. "Performance evaluation of portfolio insurance strategies using stochastic dominance criteria," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 272-280, February.
    9. Heidorn, Thomas & Siragusano, Tindaro, 2004. "Die Anwendbarkeit der Behavioral Finance im Devisenmarkt," Frankfurt School - Working Paper Series 52, Frankfurt School of Finance and Management.
    10. Suzanne Fifield & David Power & C. Donald Sinclair, 2005. "An analysis of trading strategies in eleven European stock markets," The European Journal of Finance, Taylor & Francis Journals, vol. 11(6), pages 531-548.
    11. Conrad, Jennifer & Kaul, Gautam, 1998. "An Anatomy of Trading Strategies," Review of Financial Studies, Society for Financial Studies, vol. 11(3), pages 489-519.
    12. Muhannad A. Atmeh & Ian M. Dobbs, 2006. "Technical analysis and the stochastic properties of the Jordanian stock market index return," Studies in Economics and Finance, Emerald Group Publishing, vol. 23(2), pages 119-140, June.
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