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Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non linear dependencies in stock returns

  • Alexandros E. Milionis


    (Bank of Greece and University of Aegean)

  • Evangelia Papanagiotou

    (University of the Aegean)

On several occasions technical analysis rules have been shown to have predictive power. The main purpose of this work is to decompose the predictive power of the moving average trading rule and isolate the portion that could be attributed to the possible exploitation of linear and non linear dependencies in stock returns. Data for the General Index of the Athens Stock Exchange are filtered using linear filters so that the resulting simulated “returns” exhibit no serial correlation. Applying moving average trading rules to both the original and the simulated indices and using a statistical testing procedure that takes into account the sensitivity of the performance of the trading rule as a function of moving average length, it is found that the predictive power of the trading rule is clearly weakened when applied to the simulated index indicating that a substantial part of the rule’s predictive power is due to the exploitation of linear dependencies in stock returns. It is also found that the contribution of linear dependencies in stock returns to the performance of the trading rule is increased for shorter moving average lengths.

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Paper provided by Bank of Greece in its series Working Papers with number 134.

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Length: 40
Date of creation: Jul 2011
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Handle: RePEc:bog:wpaper:134
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  1. Alexandros Milionis & Evangelia Papanagiotou, 2009. "A study of the predictive performance of the moving average trading rule as applied to NYSE, the Athens Stock Exchange and the Vienna Stock Exchange: sensitivity analysis and implications for weak-for," Applied Financial Economics, Taylor & Francis Journals, vol. 19(14), pages 1171-1186.
  2. Alexandros E. Milionis & Evangelia Papanagiotou, 2008. "On the Use of the Moving Average Trading Rule to Test for Weak Form Efficiency in Capital Markets," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 37(2), pages 181-201, 07.
  3. Theodore Panagiotidis, 2008. "Market Efficiency and the Euro: The case of the Athens Stock exchange," Discussion Paper Series 2008_14, Department of Economics, University of Macedonia, revised Dec 2008.
  4. Olson, Dennis, 2004. "Have trading rule profits in the currency markets declined over time?," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 85-105, January.
  5. Ki-Yeol Kwon & Richard Kish, 2002. "Technical trading strategies and return predictability: NYSE," Applied Financial Economics, Taylor & Francis Journals, vol. 12(9), pages 639-653.
  6. Neftci, Salih N, 1991. "Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis."," The Journal of Business, University of Chicago Press, vol. 64(4), pages 549-71, October.
  7. Dolado, Juan J & Jenkinson, Tim & Sosvilla-Rivero, Simon, 1990. " Cointegration and Unit Roots," Journal of Economic Surveys, Wiley Blackwell, vol. 4(3), pages 249-73.
  8. Fang, Yue & Xu, Daming, 2003. "The predictability of asset returns: an approach combining technical analysis and time series forecasts," International Journal of Forecasting, Elsevier, vol. 19(3), pages 369-385.
  9. F. FernAndez-RodrIguez & S. Sosvilla-Rivero & J. Andrada-FElix, 2003. "Technical analysis in foreign exchange markets: evidence from the EMS," Applied Financial Economics, Taylor & Francis Journals, vol. 13(2), pages 113-122.
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