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Testing the Profitability of Technical Analysis as a Portfolio Selection Strategy

Author

Listed:
  • Vlad Pavlov

    () (QUT)

  • Stan Hurn

    () (QUT)

Abstract

One of the main diffculties in evaluating the profits obtained using technical analysis is that trading rules are often specifed rather vaguely by practitioners and depend upon the judicious choice of rule parameters. In this paper, popular moving-average (or cross-over) rules are applied to a cross-section of Australian stocks and the signals from the rules are used to form portfolios. The performance of the trading rules across the full range of possible parameter values is evaluated by means of an aggregate test that does not depend on the parameters of the rules. The results indicate that for a wide range of parameters moving-average rules generate contrarian profits (profits from the moving-average rules are negative). In bootstrap simulations the returns statistics are significant indicating that the moving-average rules pick up some form of systematic variation in returns that does not correlate with the standard risk factors.

Suggested Citation

  • Vlad Pavlov & Stan Hurn, 2009. "Testing the Profitability of Technical Analysis as a Portfolio Selection Strategy," NCER Working Paper Series 52, National Centre for Econometric Research.
  • Handle: RePEc:qut:auncer:2009_65
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    File URL: http://www.ncer.edu.au/papers/documents/WPNo52.pdf
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    References listed on IDEAS

    as
    1. Hudson, Robert & Dempsey, Michael & Keasey, Kevin, 1996. "A note on the weak form efficiency of capital markets: The application of simple technical trading rules to UK stock prices - 1935 to 1994," Journal of Banking & Finance, Elsevier, vol. 20(6), pages 1121-1132, July.
    2. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
    3. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1770, August.
    4. Gencay, Ramazan, 1998. "The predictability of security returns with simple technical trading rules," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 347-359, October.
    5. Carol L. Osler & P.H. Kevin Chang, 1995. "Head and shoulders: not just a flaky pattern," Staff Reports 4, Federal Reserve Bank of New York.
    6. Conrad, Jennifer & Kaul, Gautam, 1998. "An Anatomy of Trading Strategies," Review of Financial Studies, Society for Financial Studies, vol. 11(3), pages 489-519.
    7. Demir, Isabelle & Muthuswamy, Jay & Walter, Terry, 2004. "Momentum returns in Australian equities: The influences of size, risk, liquidity and return computation," Pacific-Basin Finance Journal, Elsevier, vol. 12(2), pages 143-158, April.
    8. A. S. Hurn & V.Pavlov, 2008. "Momentum in Australian Stock Returns: An Update," NCER Working Paper Series 23, National Centre for Econometric Research, revised 26 Feb 2008.
    9. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. " Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    10. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    11. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. " Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    12. 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 1731-1764, December.
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    More about this item

    Keywords

    Stock returns; Technical analysis; Momentum trading rules; Bootstrapping.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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