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Testing the Profitability of Technical Analysis as a Portfolio Selection Strategy

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Author Info

  • 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.

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Bibliographic Info

Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 52.

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Length: 31
Date of creation: 09 Dec 2009
Date of revision:
Handle: RePEc:qut:auncer:2009_65

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Related research

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

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  1. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," NBER Working Papers 7613, National Bureau of Economic Research, Inc.
  2. C.L. Osler & P.H. Kevin Chang, 1995. "Head and shoulders: not just a flaky pattern," Staff Reports 4, Federal Reserve Bank of New York.
  3. 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.
  4. 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.
  5. 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.
  6. Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers 90-22, Wisconsin Madison - Social Systems.
  7. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
  8. Conrad, Jennifer & Kaul, Gautam, 1998. "An Anatomy of Trading Strategies," Review of Financial Studies, Society for Financial Studies, vol. 11(3), pages 489-519.
  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 153-81, March.
  10. 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.
  11. 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.
  12. 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.
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